2019 |
Jordao, Artur; Kloss, Ricardo; Yamada, Fernando; Schwartz, William Robson Pruning Deep Networks using Partial Least Squares Inproceedings British Machine Vision Conference (BMVC) Workshops, pp. 1-9, 2019. @inproceedings{Jordao:2019:BMVC, title = {Pruning Deep Networks using Partial Least Squares}, author = {Artur Jordao and Ricardo Kloss and Fernando Yamada and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_BMVCW_Jordao.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {British Machine Vision Conference (BMVC) Workshops}, pages = {1-9}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
de Prates, Raphael Felipe Carvalho; Schwartz, William Robson Kernel cross-view collaborative representation based classification for person re-identification Journal Article Journal of Visual Communication and Image Representation, 58 (1), pp. 304-315, 2019. Links | BibTeX | Tags: Kernel collaborative representation based classification, Person Re-Identification @article{Prates:2019:JVCI, title = {Kernel cross-view collaborative representation based classification for person re-identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, doi = {https://doi.org/10.1016/j.jvcir.2018.12.003}, year = {2019}, date = {2019-01-01}, journal = {Journal of Visual Communication and Image Representation}, volume = {58}, number = {1}, pages = {304-315}, keywords = {Kernel collaborative representation based classification, Person Re-Identification}, pubstate = {published}, tppubtype = {article} } |
Caetano, Carlos; de Melo, Victor H C; Brémond, François; dos Santos, Jefersson A; Schwartz, William Robson Magnitude-Orientation Stream network and depth information applied to activity recognition Journal Article Journal of Visual Communication and Image Representation, 63 , pp. 102596, 2019, ISSN: 1047-3203. Resumo | Links | BibTeX | Tags: @article{CAETANO2019102596, title = {Magnitude-Orientation Stream network and depth information applied to activity recognition}, author = {Carlos Caetano and Victor H C de Melo and François Brémond and Jefersson A dos Santos and William Robson Schwartz}, url = {http://www.sciencedirect.com/science/article/pii/S1047320319302172}, doi = {https://doi.org/10.1016/j.jvcir.2019.102596}, issn = {1047-3203}, year = {2019}, date = {2019-01-01}, journal = {Journal of Visual Communication and Image Representation}, volume = {63}, pages = {102596}, abstract = {The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Moreover, we also employ depth information to use as a weighting scheme on the magnitude information to compensate the distance of the subjects performing the activity to the camera. Experimental results, carried on two well-known datasets (UCF101 and NTU), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Moreover, we also employ depth information to use as a weighting scheme on the magnitude information to compensate the distance of the subjects performing the activity to the camera. Experimental results, carried on two well-known datasets (UCF101 and NTU), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation. |
Goncalves, Gabriel Resende; Diniz, Matheus Alves; Laroca, Rayson; Menotti, David; Schwartz, William Robson Multi-Task Learning for Low-Resolution License Plate Recognition Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 1-10, 2019. @inproceedings{Goncalves:2019:CIARP, title = {Multi-Task Learning for Low-Resolution License Plate Recognition}, author = {Gabriel Resende Goncalves and Matheus Alves Diniz and Rayson Laroca and David Menotti and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_CIARP_Goncalves.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, pages = {1-10}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Vareto, Rafael Henrique; Diniz, Matheus Alves; Schwartz, William Robson Face Spoofing Detection on Low-Power Devices using Embeddings with Spatial and Frequency-based Descriptors Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 1-10, 2019. @inproceedings{Vareto:2019:CIARP, title = {Face Spoofing Detection on Low-Power Devices using Embeddings with Spatial and Frequency-based Descriptors}, author = {Rafael Henrique Vareto and Matheus Alves Diniz and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_CIARP_Vareto.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, pages = {1-10}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
de Lima, Vitor Cezar; Schwartz, William Robson Gait Recognition using Pose Estimation and Signal Processing Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 1-10, 2019. @inproceedings{Lima:2019:CIARP, title = {Gait Recognition using Pose Estimation and Signal Processing}, author = {Vitor Cezar de Lima and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_CIARP_Vitor.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, pages = {1-10}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Bastos, Igor; de Melo, Victor Hugo Cunha; Schwartz, William Robson Multi-Loss Recurrent Residual Networks for Gesture Detection and Recognition Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2019. @inproceedings{Bastos:2019:SIBGRAPIb, title = {Multi-Loss Recurrent Residual Networks for Gesture Detection and Recognition}, author = {Igor Bastos and Victor Hugo Cunha de Melo and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_SIBGRAPI_Bastos.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Mendes, G; Paiva, Jose Gustavo; Schwartz, William Robson Point-placement techniques and Temporal Self-similarity Maps for Visual Analysis of Surveillance Videos Inproceedings International Conference Information Visualisation, pp. 1-8, 2019. BibTeX | Tags: @inproceedings{Mendes:2019:ICIV, title = {Point-placement techniques and Temporal Self-similarity Maps for Visual Analysis of Surveillance Videos}, author = {G Mendes and Jose Gustavo Paiva and William Robson Schwartz}, year = {2019}, date = {2019-01-01}, booktitle = {International Conference Information Visualisation}, pages = {1-8}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Caetano, Carlos; Bremond, Francois; Schwartz, William Robson Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2019. @inproceedings{Caetano:2019:SIBGRAPIb, title = {Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints}, author = {Carlos Caetano and Francois Bremond and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_SIBGRAPI_Caetano.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Caetano, Carlos; Souza, Jessica; Bremond, Francois; Santos, Jefersson; Schwartz, William Robson SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition Inproceedings 16th International Conference on Advanced Video and Signal-based Surveillance (AVSS), pp. 1-6, 2019. @inproceedings{Caetano:2019:AVSSb, title = {SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition}, author = {Carlos Caetano and Jessica Souza and Francois Bremond and Jefersson Santos and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_AVSS_Caetano.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {16th International Conference on Advanced Video and Signal-based Surveillance (AVSS)}, pages = {1-6}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2018 |
de Melo, Victor Hugo Cunha; Santos, Jesimon Barreto; Junior, Carlos Antonio Caetano; Sena, Jessica; Penatti, Otavio A B; Schwartz, William Robson Object-based Temporal Segment Relational Network for Activity Recognition Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. BibTeX | Tags: Activity Recognition, DeepEyes, HAR-HEALTH, Relational Reasoning, Spatial Pyramid @inproceedings{DeMelo:2018:SIBGRAPI, title = {Object-based Temporal Segment Relational Network for Activity Recognition}, author = {Victor Hugo Cunha de Melo and Jesimon Barreto Santos and Carlos Antonio Caetano Junior and Jessica Sena and Otavio A B Penatti and William Robson Schwartz}, year = {2018}, date = {2018-09-21}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Activity Recognition, DeepEyes, HAR-HEALTH, Relational Reasoning, Spatial Pyramid}, pubstate = {published}, tppubtype = {inproceedings} } |
Sena, Jessica; Santos, Jesimon Barreto; Schwartz, William Robson Multiscale DCNN Ensemble Applied to Human Activity Recognition Based on Wearable Sensors Inproceedings 26th European Signal Processing Conference (EUSIPCO 2018), pp. 1-5, 2018. Links | BibTeX | Tags: Activity Recognition Based on Wearable Sensors, Deep Learning, DeepEyes, HAR-HEALTH, Human Activity Recognition, Multimodal Data, Wearable Sensors @inproceedings{Sena:2018:EUSIPCO, title = {Multiscale DCNN Ensemble Applied to Human Activity Recognition Based on Wearable Sensors}, author = {Jessica Sena and Jesimon Barreto Santos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/PID5428933.pdf}, year = {2018}, date = {2018-09-06}, booktitle = {26th European Signal Processing Conference (EUSIPCO 2018)}, pages = {1-5}, keywords = {Activity Recognition Based on Wearable Sensors, Deep Learning, DeepEyes, HAR-HEALTH, Human Activity Recognition, Multimodal Data, Wearable Sensors}, pubstate = {published}, tppubtype = {inproceedings} } |
Gonçalves, Gabriel Resende; Diniz, Matheus Alves; Laroca, Rayson; Menotti, David; Schwartz, William Robson Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. Links | BibTeX | Tags: Automatic License Plate Recognition, Deep Learning, DeepEyes, GigaFrames, Multi-Task Learning, Sense-ALPR @inproceedings{Goncalves:2018:SIBGRAPI, title = {Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks}, author = {Gabriel Resende Gonçalves and Matheus Alves Diniz and Rayson Laroca and David Menotti and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper.pdf}, year = {2018}, date = {2018-09-04}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Automatic License Plate Recognition, Deep Learning, DeepEyes, GigaFrames, Multi-Task Learning, Sense-ALPR}, pubstate = {published}, tppubtype = {inproceedings} } |
de Prates, Raphael Felipe Carvalho; Schwartz, William Robson Kernel multiblock partial least squares for a scalable and multicamera person reidentification system Journal Article pp. 1-33, 2018. Links | BibTeX | Tags: Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification @article{Prates:2018:JEI, title = {Kernel multiblock partial least squares for a scalable and multicamera person reidentification system}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/article.pdf}, year = {2018}, date = {2018-06-25}, booktitle = {Journal of Electronic Imaging}, pages = {1-33}, keywords = {Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {article} } |
Junior, Carlos Antonio Caetano; dos Santos, Jefersson A; Schwartz, William Robson Statistical Measures from Co-occurrence of Codewords for Action Recognition Inproceedings VISAPP 2018 - International Conference on Computer Vision Theory and Applications, pp. 1-8, 2018. Links | BibTeX | Tags: Action Recognition, Activity Recognition, DeepEyes, GigaFrames, Spatiotemporal Features @inproceedings{Caetano:2018:VISAPP, title = {Statistical Measures from Co-occurrence of Codewords for Action Recognition}, author = {Carlos Antonio Caetano Junior and Jefersson A dos Santos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/VISAPP_2018_CarlosCaetano.pdf}, year = {2018}, date = {2018-01-27}, booktitle = {VISAPP 2018 - International Conference on Computer Vision Theory and Applications}, pages = {1-8}, keywords = {Action Recognition, Activity Recognition, DeepEyes, GigaFrames, Spatiotemporal Features}, pubstate = {published}, tppubtype = {inproceedings} } |
Laroca, R; Severo, E; Zanlorensi, L A; Oliveira, L S; Gon�alves, G R; Schwartz, W R; Menotti, D A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector Inproceedings 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-10, 2018. @inproceedings{Laroca:2018:IJCNN, title = {A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector}, author = {R Laroca and E Severo and L A Zanlorensi and L S Oliveira and G R Gon�alves and W R Schwartz and D Menotti}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2018_IJCNN_Laroca.pdf}, doi = {10.1109/IJCNN.2018.8489629}, year = {2018}, date = {2018-01-01}, booktitle = {2018 International Joint Conference on Neural Networks (IJCNN)}, pages = {1-10}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ferreira, Gabriel Sousa; Padua, Flavio Luis Cardeal; Schwartz, William Robson; Rodrigues, Marco Tulio Alves Nolasco Mapping Sports Interest with Social Network Inproceedings XV Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC), pp. 1-12, 2018. @inproceedings{Ferreira:2018:ENIAC, title = {Mapping Sports Interest with Social Network}, author = {Gabriel Sousa Ferreira and Flavio Luis Cardeal Padua and William Robson Schwartz and Marco Tulio Alves Nolasco Rodrigues}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2018_ENIAC.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {XV Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC)}, pages = {1-12}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Kloss, Ricardo Barbosa; Jordao, Artur; Schwartz, William Robson Face Verification Strategies for Employing Deep Models Inproceedings 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 258-262, 2018. Links | BibTeX | Tags: Artificial Neural Networks, Face Verification, GigaFrames, Metric Learning, Transfer Learning @inproceedings{Kloss:2018:FG, title = {Face Verification Strategies for Employing Deep Models}, author = {Ricardo Barbosa Kloss and Artur Jordao and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Face-Verification-Strategies-for-Employing-Deep-Models.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {13th IEEE International Conference on Automatic Face & Gesture Recognition}, pages = {258-262}, keywords = {Artificial Neural Networks, Face Verification, GigaFrames, Metric Learning, Transfer Learning}, pubstate = {published}, tppubtype = {inproceedings} } |
Jordao, Artur; Junior, Antonio Carlos Nazare; Sena, Jessica; Schwartz, William Robson Human Activity Recognition based on Wearable Sensor Data: A Benchmark Journal Article arXiv, pp. 1-12, 2018. Links | BibTeX | Tags: Activity Recognition Based on Wearable Sensors, Benchmark on Activity Recognition based on Wearable Sensors, Wearable Sensors @article{Jordao:2018:arXiv, title = {Human Activity Recognition based on Wearable Sensor Data: A Benchmark}, author = {Artur Jordao and Antonio Carlos Nazare Junior and Jessica Sena and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/Human-Activity-Recognition-based-on-Wearable-A-Benchmark.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {Arvix}, journal = {arXiv}, pages = {1-12}, keywords = {Activity Recognition Based on Wearable Sensors, Benchmark on Activity Recognition based on Wearable Sensors, Wearable Sensors}, pubstate = {published}, tppubtype = {article} } |
Jordao, Artur; Kloss, Ricardo Barbosa; Schwartz, William Robson Latent hypernet: Exploring all Layers from Convolutional Neural Networks Inproceedings IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2018. Links | BibTeX | Tags: Activity Recognition Based on Wearable Sensors, DeepEyes, GigaFrames, Partial Least Squares, Wearable Sensors @inproceedings{Jordao:2018b:IJCNN, title = {Latent hypernet: Exploring all Layers from Convolutional Neural Networks}, author = {Artur Jordao and Ricardo Barbosa Kloss and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Latent-HyperNet-Exploring-the-Layers.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {IEEE International Joint Conference on Neural Networks (IJCNN)}, pages = {1-7}, keywords = {Activity Recognition Based on Wearable Sensors, DeepEyes, GigaFrames, Partial Least Squares, Wearable Sensors}, pubstate = {published}, tppubtype = {inproceedings} } |
Jordao, Artur; Torres, Leonardo Antônio Borges; Schwartz, William Robson Novel Approaches to Human Activity Recognition based on Accelerometer Data Journal Article 12 (7), pp. 1387–1394, 2018. Links | BibTeX | Tags: Activity Recognition Based on Wearable Sensors, HAR-HEALTH, Wearable Sensors @article{Jordao:2018:SIVP, title = {Novel Approaches to Human Activity Recognition based on Accelerometer Data}, author = {Artur Jordao and Leonardo Antônio Borges Torres and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Novel-Approaches-to-Human-Activity-Recognition-based-on.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {Signal, Image and Video Processing}, volume = {12}, number = {7}, pages = {1387–1394}, keywords = {Activity Recognition Based on Wearable Sensors, HAR-HEALTH, Wearable Sensors}, pubstate = {published}, tppubtype = {article} } |
Colque, Rensso Victor Hugo Mora; Junior, Carlos Antonio Caetano; de Melo, Victor Hugo Cunha; Chavez, Guillermo Camara; Schwartz, William Robson Novel Anomalous Event Detection based on Human-object Interactions Inproceedings VISAPP 2018 - International Conference on Computer Vision Theory and Applications, pp. 1-8, 2018. Links | BibTeX | Tags: Anomalous Event Detection, Contextual Information, DeepEyes, GigaFrames, Human-Object Interaction @inproceedings{Colque:2018:VISAPP, title = {Novel Anomalous Event Detection based on Human-object Interactions}, author = {Rensso Victor Hugo Mora Colque and Carlos Antonio Caetano Junior and Victor Hugo Cunha de Melo and Guillermo Camara Chavez and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/VISAPP_2018_92.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {VISAPP 2018 - International Conference on Computer Vision Theory and Applications}, pages = {1-8}, keywords = {Anomalous Event Detection, Contextual Information, DeepEyes, GigaFrames, Human-Object Interaction}, pubstate = {published}, tppubtype = {inproceedings} } |
Bastos, Igor Leonardo Oliveira; de Melo, Victor Hugo Cunha; Gonçalves, Gabriel Resende; Schwartz, William Robson MORA: A Generative Approach to Extract Spatiotemporal Information Applied to Gesture Recognition Inproceedings 15th International Conference on Advanced Video and Signal-based Surveillance (AVSS), pp. 1-6, 2018. Links | BibTeX | Tags: Autoencoders, DeepEyes, Gesture Recognition, GigaFrames, Recurrent Models @inproceedings{Bastos:2018:AVSS, title = {MORA: A Generative Approach to Extract Spatiotemporal Information Applied to Gesture Recognition}, author = {Igor Leonardo Oliveira Bastos and Victor Hugo Cunha de Melo and Gabriel Resende Gonçalves and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/MORA_.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {15th International Conference on Advanced Video and Signal-based Surveillance (AVSS)}, pages = {1-6}, keywords = {Autoencoders, DeepEyes, Gesture Recognition, GigaFrames, Recurrent Models}, pubstate = {published}, tppubtype = {inproceedings} } |
Kloss, Ricardo Barbosa; Jordao, Artur; Schwartz, William Robson Boosted Projection An Ensemble of Transformation Models Inproceedings 22nd Iberoamerican Congress on Pattern Recognition (CIARP), pp. 331-338, 2018. Links | BibTeX | Tags: Computer vision, DeepEyes, Dimensionality Reduction, Ensemble Partial Least Squares, GigaFrames, Machine Learning @inproceedings{Kloss:2018:CIARP, title = {Boosted Projection An Ensemble of Transformation Models}, author = {Ricardo Barbosa Kloss and Artur Jordao and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Boosted-Projection-An-Ensemble-of-Transformation-Models.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {22nd Iberoamerican Congress on Pattern Recognition (CIARP)}, pages = {331-338}, keywords = {Computer vision, DeepEyes, Dimensionality Reduction, Ensemble Partial Least Squares, GigaFrames, Machine Learning}, pubstate = {published}, tppubtype = {inproceedings} } |
Reis, Renan Oliveira; Dias, Igor Henrique; Schwartz, William Robson Neural network control for active cameras using master-slave setup Inproceedings International Conference on Advanced Video and Signal-based Surveillance (AVSS), pp. 1-6, 2018. Links | BibTeX | Tags: Active Camera, DeepEyes, GigaFrames, Neural network control for active cameras using master-slave setup, SMS @inproceedings{Reis:2018:AVSS, title = {Neural network control for active cameras using master-slave setup}, author = {Renan Oliveira Reis and Igor Henrique Dias and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/renan_avss_2018-1-1.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {International Conference on Advanced Video and Signal-based Surveillance (AVSS)}, pages = {1-6}, keywords = {Active Camera, DeepEyes, GigaFrames, Neural network control for active cameras using master-slave setup, SMS}, pubstate = {published}, tppubtype = {inproceedings} } |
Junior, Antonio Carlos Nazare; de Costa, Filipe Oliveira; Schwartz, William Robson Content-Based Multi-Camera Video Alignment using Accelerometer Data Inproceedings Advanced Video and Signal Based Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 1-6, 2018. Links | BibTeX | Tags: Camera Synchronization, DeepEyes, GigaFrames, SensorCap, Sensors, SMS @inproceedings{Nazare:2018:AVSS, title = {Content-Based Multi-Camera Video Alignment using Accelerometer Data}, author = {Antonio Carlos Nazare Junior and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2018_avss_svsync_camera_ready.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {Advanced Video and Signal Based Surveillance (AVSS), 2018 15th IEEE International Conference on}, pages = {1-6}, keywords = {Camera Synchronization, DeepEyes, GigaFrames, SensorCap, Sensors, SMS}, pubstate = {published}, tppubtype = {inproceedings} } |
Jordao, Artur; Kloss, Ricardo; Yamada, Fernando; Schwartz, William Robson Pruning Deep Neural Networks using Partial Least Squares Journal Article ArXiv e-prints, 2018. Links | BibTeX | Tags: DeepEyes, GigaFrames, Neural Networks Optimization @article{Jordao:2018:arXivb, title = {Pruning Deep Neural Networks using Partial Least Squares}, author = {Artur Jordao and Ricardo Kloss and Fernando Yamada and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/1810.07610.pdf}, year = {2018}, date = {2018-01-01}, journal = {ArXiv e-prints}, keywords = {DeepEyes, GigaFrames, Neural Networks Optimization}, pubstate = {published}, tppubtype = {article} } |
Prates, Raphael; Schwartz, William Robson Kernel Multiblock Partial Least Squares for a Scalable and Multicamera Person Reidentification System Journal Article Journal of Electronic Imaging, 27 (3), pp. 1-33, 2018. @article{Prates:2018:JEIb, title = {Kernel Multiblock Partial Least Squares for a Scalable and Multicamera Person Reidentification System}, author = {Raphael Prates and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2018_JEI.pdf}, doi = {10.1117/1.JEI.27.3.033041}, year = {2018}, date = {2018-01-01}, journal = {Journal of Electronic Imaging}, volume = {27}, number = {3}, pages = {1-33}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Sales, Anderson Luis Cavalcanti; Vareto, Rafael Henrique; Schwartz, William Robson; Chavez, Guillermo Camara Single-Shot Person Re-Identification Combining Similarity Metrics and Support Vectors Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. @inproceedings{Sales:2018:SIBGRAPI, title = {Single-Shot Person Re-Identification Combining Similarity Metrics and Support Vectors}, author = {Anderson Luis Cavalcanti Sales and Rafael Henrique Vareto and William Robson Schwartz and Guillermo Camara Chavez}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2018_SIBGRAPI_Sales.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2017 |
Bastos, Igor Leonardo Oliveira; Soares, Larissa Rocha; Schwartz, William Robson Pyramidal Zernike Over Time: A spatiotemporal feature descriptor based on Zernike Moments Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP 2017), pp. 77-85, 2017. Links | BibTeX | Tags: Activity Recognition, DeepEyes, Feature Extraction, GigaFrames, Zernike Moments @inproceedings{Bastos:2017:CIARP, title = {Pyramidal Zernike Over Time: A spatiotemporal feature descriptor based on Zernike Moments}, author = {Igor Leonardo Oliveira Bastos and Larissa Rocha Soares and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/PZOT_camera_ready.pdf}, year = {2017}, date = {2017-11-07}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP 2017)}, pages = {77-85}, keywords = {Activity Recognition, DeepEyes, Feature Extraction, GigaFrames, Zernike Moments}, pubstate = {published}, tppubtype = {inproceedings} } |
Vareto, Rafael Henrique; da Silva, Samira Santos; de Costa, Filipe Oliveira; Schwartz, William Robson Towards Open-Set Face Recognition using Hashing Functions Inproceedings International Joint Conference on Biometrics (IJCB), pp. 1-8, 2017. Links | BibTeX | Tags: Face Recognition, Open-Set Classification @inproceedings{Vareto:2017:IJCB, title = {Towards Open-Set Face Recognition using Hashing Functions}, author = {Rafael Henrique Vareto and Samira Santos da Silva and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_IJCB.pdf http://lena.dcc.ufmg.br/wordpress/towards-open-set-face-recognition-using-hashing-functions/}, year = {2017}, date = {2017-01-01}, booktitle = {International Joint Conference on Biometrics (IJCB)}, pages = {1-8}, keywords = {Face Recognition, Open-Set Classification}, pubstate = {published}, tppubtype = {inproceedings} } |
Colque, Rensso Victor Hugo Mora; Junior, Carlos Antonio Caetano; de Andrade, Matheus Toledo Lustosa; Schwartz, William Robson Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos Journal Article IEEE Transactions on Circuits and Systems for Video Technology, 27 (3), pp. 673-682, 2017. Links | BibTeX | Tags: Anomalous Event Detection, DeepEyes, Feature Extraction, GigaFrames @article{Colque:2016:TCSVT, title = {Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos}, author = {Rensso Victor Hugo Mora Colque and Carlos Antonio Caetano Junior and Matheus Toledo Lustosa de Andrade and William Robson Schwartz}, url = {http://dx.doi.org/10.1109/TCSVT.2016.2637778}, year = {2017}, date = {2017-01-01}, journal = {IEEE Transactions on Circuits and Systems for Video Technology}, volume = {27}, number = {3}, pages = {673-682}, keywords = {Anomalous Event Detection, DeepEyes, Feature Extraction, GigaFrames}, pubstate = {published}, tppubtype = {article} } |
Bastos, Igor Leonardo Oliveira; Schwartz, William Robson Assigning Relative Importance to Scene Elements Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2017. Links | BibTeX | Tags: Assigning Relative Importance to Scene Elements, Importance Annotation for VIP and UIUC Pascal Sentence Datasets, Importance Assignment @inproceedings{Bastos:2017:SIBGRAPI, title = {Assigning Relative Importance to Scene Elements}, author = {Igor Leonardo Oliveira Bastos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_SIBGRAPI_Bastos.pdf}, year = {2017}, date = {2017-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Assigning Relative Importance to Scene Elements, Importance Annotation for VIP and UIUC Pascal Sentence Datasets, Importance Assignment}, pubstate = {published}, tppubtype = {inproceedings} } |
Junior, Carlos Antonio Caetano; de Melo, Victor Hugo Cunha; dos Santos, Jefersson Alex; Schwartz, William Robson Activity Recognition based on a Magnitude-Orientation Stream Network Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2017. Links | BibTeX | Tags: Activity Recognition, Deep Learning, GigaFrames @inproceedings{Caetano:2017:SIBGRAPI, title = {Activity Recognition based on a Magnitude-Orientation Stream Network}, author = {Carlos Antonio Caetano Junior and Victor Hugo Cunha de Melo and Jefersson Alex dos Santos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_SIBGRAPI_Caetano.pdf}, year = {2017}, date = {2017-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Activity Recognition, Deep Learning, GigaFrames}, pubstate = {published}, tppubtype = {inproceedings} } |
Vareto, Rafael Henrique; da Silva, Samira Santos; de Costa, Filipe Oliveira; Schwartz, William Robson Face Verification based on Relational Disparity Features and Partial Least Squares Models Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2017. Links | BibTeX | Tags: Face Recognition, Face Verification @inproceedings{Vareto:2017:SIBGRAPI, title = {Face Verification based on Relational Disparity Features and Partial Least Squares Models}, author = {Rafael Henrique Vareto and Samira Santos da Silva and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_SIBGRAPI_Vareto.pdf http://lena.dcc.ufmg.br/wordpress/face-verification-based-relational-disparity-features-partial-least-squares-models/}, year = {2017}, date = {2017-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Face Recognition, Face Verification}, pubstate = {published}, tppubtype = {inproceedings} } |
2016 |
de Prates, Raphael Felipe Carvalho; Schwartz, William Robson Kernel Hierarchical PCA for Person Re-Identification Inproceedings IAPR International Conference on Pattern Recognition (ICPR), 2016. Resumo | Links | BibTeX | Tags: Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification @inproceedings{Prates2016ICPR, title = {Kernel Hierarchical PCA for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Kernel_HPCA_final.pdf}, year = {2016}, date = {2016-12-13}, booktitle = {IAPR International Conference on Pattern Recognition (ICPR)}, abstract = {Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person ReID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions.}, keywords = {Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person ReID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions. |
Jordao, Artur; Sena, Jessica; Schwartz, William Robson A Late Fusion Approach to Combine Multiple Pedestrian Detectors Inproceedings IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016. Links | BibTeX | Tags: DeepEyes, Featured Publication, GigaFrames, Pedestrian Detection @inproceedings{Correia:2016:ICPR, title = {A Late Fusion Approach to Combine Multiple Pedestrian Detectors}, author = {Artur Jordao and Jessica Sena and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/A-Late-Fusion-Approach-to-Combine-Multiple.pdf}, year = {2016}, date = {2016-12-13}, booktitle = {IAPR International Conference on Pattern Recognition (ICPR)}, pages = {1-6}, keywords = {DeepEyes, Featured Publication, GigaFrames, Pedestrian Detection}, pubstate = {published}, tppubtype = {inproceedings} } |
Junior, Carlos Antonio Caetano; dos Santos, Jefersson A; Schwartz, William Robson Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor Inproceedings IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016. Links | BibTeX | Tags: Activity Recognition, Descriptor, Feature Extraction, Featured Publication, OFCM, VER+ @inproceedings{Caetano:2016:ICPR, title = {Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor}, author = {Carlos Antonio Caetano Junior and Jefersson A dos Santos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/ICPR2016.pdf}, year = {2016}, date = {2016-12-13}, booktitle = {IAPR International Conference on Pattern Recognition (ICPR)}, pages = {1-6}, keywords = {Activity Recognition, Descriptor, Feature Extraction, Featured Publication, OFCM, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Gonçalves, Gabriel Resende; Menotti, David; Schwartz, William Robson License Plate Recognition based on Temporal Redundancy Inproceedings IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1-5, 2016. Links | BibTeX | Tags: Automatic License Plate Recognition, DeepEyes, GigaFrames @inproceedings{Goncalves:2016:ITSC, title = {License Plate Recognition based on Temporal Redundancy}, author = {Gabriel Resende Gonçalves and David Menotti and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_ITSC.pdf}, year = {2016}, date = {2016-11-04}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, pages = {1-5}, keywords = {Automatic License Plate Recognition, DeepEyes, GigaFrames}, pubstate = {published}, tppubtype = {inproceedings} } |
Gonçalves, Gabriel Resende; da Silva, Sirlene Pio Gomes; Menotti, David; Schwartz, William Robson Benchmark for License Plate Character Segmentation Journal Article Journal of Electronic Imaging, 25 (5), pp. 1-5, 2016, ISBN: 1017-9909. Links | BibTeX | Tags: Automatic License Plate Recognition, Benchmark, Character Segmentation, DeepEyes, Featured Publication, GigaFrames, Jaccard Coefficient, Novel Dataset, Sense SegPlate @article{2016:JEI:Gabriel, title = {Benchmark for License Plate Character Segmentation}, author = {Gabriel Resende Gonçalves and Sirlene Pio Gomes da Silva and David Menotti and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/JEI-2016-Benchmark.pdf}, isbn = {1017-9909}, year = {2016}, date = {2016-10-24}, journal = {Journal of Electronic Imaging}, volume = {25}, number = {5}, pages = {1-5}, keywords = {Automatic License Plate Recognition, Benchmark, Character Segmentation, DeepEyes, Featured Publication, GigaFrames, Jaccard Coefficient, Novel Dataset, Sense SegPlate}, pubstate = {published}, tppubtype = {article} } |
Vareto, Rafael Henrique; de Costa, Filipe Oliveira; Schwartz, William Robson Face Identification in Large Galleries Inproceedings Workshop on Face Processing Applications, pp. 1-4, 2016. Links | BibTeX | Tags: DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+ @inproceedings{Vareto:2016:WFPA, title = {Face Identification in Large Galleries}, author = {Rafael Henrique Vareto and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_WFPA.pdf}, year = {2016}, date = {2016-10-02}, booktitle = {Workshop on Face Processing Applications}, pages = {1-4}, keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
de Prates, Raphael Felipe Carvalho; Dutra, Cristianne Rodrigues Santos; Schwartz, William Robson Predominant Color Name Indexing Structure for Person Re-Identification Inproceedings IEEE International Conference on Image Processing (ICIP), 2016. Resumo | Links | BibTeX | Tags: GigaFrames, Person Re-Identification, VER+ @inproceedings{Prates2016ICIP, title = {Predominant Color Name Indexing Structure for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and Cristianne Rodrigues Santos Dutra and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_ICIP_Prates.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, abstract = {The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery.}, keywords = {GigaFrames, Person Re-Identification, VER+}, pubstate = {published}, tppubtype = {inproceedings} } The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery. |
de Prates, Raphael Felipe Carvalho; Oliveira, Marina Santos; Schwartz, William Robson Kernel Partial Least Squares for Person Re-Identification Inproceedings IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016. Resumo | Links | BibTeX | Tags: DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification @inproceedings{Prates2016AVSS, title = {Kernel Partial Least Squares for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and Marina Santos Oliveira and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/egpaper_for_DoubleBlindReview.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)}, abstract = {Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S.}, keywords = {DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S. |
Jordao, Artur; Schwartz, William Robson Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection Inproceedings IEEE International Conference on Image Processing (ICIP), pp. 2931-2935, 2016. Links | BibTeX | Tags: DeepEyes, Pedestrian Detection, VER+ @inproceedings{Correia:2016:ICIP, title = {Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection}, author = {Artur Jordao and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/OBLIQUE-RANDOM-FOREST-BASED-ON-PARTIAL-LEAST-SQUARES-APPLIED-TO.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, pages = {2931-2935}, keywords = {DeepEyes, Pedestrian Detection, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Goncalves, Gabriel Resende License Plate Recognition based on Temporal Redundancy Masters Thesis Federal University of Minas Gerais, 2016. Resumo | Links | BibTeX | Tags: Automatic License Plate Recognition, DeepEyes, GigaFrames @mastersthesis{Goncalves:2016:MSc, title = {License Plate Recognition based on Temporal Redundancy}, author = {Gabriel Resende Goncalves}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_Gabriel.pdf}, year = {2016}, date = {2016-08-26}, school = {Federal University of Minas Gerais}, abstract = {Recognition of vehicle license plates is an important task applied to a myriad of real scenarios. Most approaches in the literature first detect an on-track vehicle, locate the license plate, perform a segmentation of its characters and then recognize the characters using an Optical Character Recognition (OCR) approach. However, these approaches focus on performing these tasks using only a single frame of each vehicle in the video. Therefore, such techniques might have their recognition rates reduced due to noise present in that particular frame. On the other hand, in this work we propose an approach to automatically detect the vehicle on the road and identify (locate/recognize) its license plate based on temporal redundant information instead of selecting a single frame to perform the recognition. We also propose two post-processing steps that can be employed to improve the accuracy of the system by querying a license plate database (e.g., the Department of Motor Vehicles database containing a list of all issued license plates and car models). Experimental results demonstrate that it is possible to improve the vehicle recognition rate in 15.5 percentage points (p.p.) (an increase of 23.38%) of the baseline results, using our proposal temporal redundancy approach. Furthermore, additional 7.8 p.p. are achieved using the two post-processing approaches, leading to a final recognition rate of 89.6% on a dataset with 5,200 frame images of $300$ vehicles recorded at Federal University of Minas Gerais (UFMG). In addition, this work also proposes a novel benchmark, designed specifically to evaluate character segmentation techniques, composed of a dataset of 2,000 Brazilian license plates (resulting in 14,000 alphanumeric symbols) and an evaluation protocol considering a novel evaluation measure, the Jaccard-Centroid coefficient.}, keywords = {Automatic License Plate Recognition, DeepEyes, GigaFrames}, pubstate = {published}, tppubtype = {mastersthesis} } Recognition of vehicle license plates is an important task applied to a myriad of real scenarios. Most approaches in the literature first detect an on-track vehicle, locate the license plate, perform a segmentation of its characters and then recognize the characters using an Optical Character Recognition (OCR) approach. However, these approaches focus on performing these tasks using only a single frame of each vehicle in the video. Therefore, such techniques might have their recognition rates reduced due to noise present in that particular frame. On the other hand, in this work we propose an approach to automatically detect the vehicle on the road and identify (locate/recognize) its license plate based on temporal redundant information instead of selecting a single frame to perform the recognition. We also propose two post-processing steps that can be employed to improve the accuracy of the system by querying a license plate database (e.g., the Department of Motor Vehicles database containing a list of all issued license plates and car models). Experimental results demonstrate that it is possible to improve the vehicle recognition rate in 15.5 percentage points (p.p.) (an increase of 23.38%) of the baseline results, using our proposal temporal redundancy approach. Furthermore, additional 7.8 p.p. are achieved using the two post-processing approaches, leading to a final recognition rate of 89.6% on a dataset with 5,200 frame images of $300$ vehicles recorded at Federal University of Minas Gerais (UFMG). In addition, this work also proposes a novel benchmark, designed specifically to evaluate character segmentation techniques, composed of a dataset of 2,000 Brazilian license plates (resulting in 14,000 alphanumeric symbols) and an evaluation protocol considering a novel evaluation measure, the Jaccard-Centroid coefficient. |
Jordao, Artur The Good, the Fast and the Better Pedestrian Detector Masters Thesis Federal University of Minas Gerais, 2016. Resumo | Links | BibTeX | Tags: DeepEyes, DET, GigaFrames, Pedestrian Detection, VER+ @mastersthesis{Jordao:2016:MSc, title = {The Good, the Fast and the Better Pedestrian Detector}, author = {Artur Jordao}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_ArturJordao.pdf}, year = {2016}, date = {2016-06-24}, school = {Federal University of Minas Gerais}, abstract = {Pedestrian detection is a well-known problem in Computer Vision, mostly because of its direct applications in surveillance, transit safety and robotics. In the past decade, several efforts have been performed to improve the detection in terms of accuracy, speed and feature enhancement. In this work, we propose and analyze techniques focusing on these points. First, we develop an accurate oblique random forest (oRF) associated with Partial Least Squares (PLS). The method utilizes the PLS to find a decision surface, at each node of a decision tree, that better splits the samples presented to it, based on some purity criterion. To measure the advantages provided by PLS on the oRF, we compare the proposed method with the oRF based on SVM. Second, we evaluate and compare filtering approaches to reduce the search space and keep only potential regions of interest to be presented to detectors, speeding up the detection process. Experimental results demonstrate that the evaluated filters are able to discard a large number of detection windows without compromising the accuracy. Finally, we propose a novel approach to extract powerful features regarding the scene. The method combines results of distinct pedestrian detectors by reinforcing the human hypothesis, whereas suppressing a significant number of false positives due to the lack of spatial consensus when multiple detectors are considered. Our proposed approach, referred to as Spatial Consensus (SC), outperforms all previously published state-of-the-art pedestrian detection methods.}, keywords = {DeepEyes, DET, GigaFrames, Pedestrian Detection, VER+}, pubstate = {published}, tppubtype = {mastersthesis} } Pedestrian detection is a well-known problem in Computer Vision, mostly because of its direct applications in surveillance, transit safety and robotics. In the past decade, several efforts have been performed to improve the detection in terms of accuracy, speed and feature enhancement. In this work, we propose and analyze techniques focusing on these points. First, we develop an accurate oblique random forest (oRF) associated with Partial Least Squares (PLS). The method utilizes the PLS to find a decision surface, at each node of a decision tree, that better splits the samples presented to it, based on some purity criterion. To measure the advantages provided by PLS on the oRF, we compare the proposed method with the oRF based on SVM. Second, we evaluate and compare filtering approaches to reduce the search space and keep only potential regions of interest to be presented to detectors, speeding up the detection process. Experimental results demonstrate that the evaluated filters are able to discard a large number of detection windows without compromising the accuracy. Finally, we propose a novel approach to extract powerful features regarding the scene. The method combines results of distinct pedestrian detectors by reinforcing the human hypothesis, whereas suppressing a significant number of false positives due to the lack of spatial consensus when multiple detectors are considered. Our proposed approach, referred to as Spatial Consensus (SC), outperforms all previously published state-of-the-art pedestrian detection methods. |
Rodrigues, Marco Tulio Alves Detecção de Mudanças em Cenas Terrestres usando Imagens Aéreas Tese PhD 2016. Resumo | Links | BibTeX | Tags: Change Detection @phdthesis{Rodrigues:2016:PhD, title = {Detecção de Mudanças em Cenas Terrestres usando Imagens Aéreas}, author = {Marco Tulio Alves Rodrigues}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_MarcoTulio.pdf}, year = {2016}, date = {2016-03-22}, abstract = {This study addresses the problem of change detection in landscapes using aerial images acquired at different times, important for many applications. The monitoring pipeline, for instance, the usual way to change detection is a task performed by human operators which evaluates a video of a monitoring camera and searches for changes in the scene from the comparison of image pairs. This procedure is prone to errors because it is a tedious task, therefore is a justification to automated method. Besides enabling the reduction of errors and speed up the monitoring process, a system automatic can be used as a filter to provide a set of key frames that should receive more attention from the operator. Thus, the system can help the operators on the decision making process regarding the actions to be performed. The basic procedure for detecting changes is to find a set of pixels or regions that are different in another test image. However, images acquired at different dates may be influenced by radiometric and registration factors. In other words, the influence of camera movement, lighting variation, and atmospheric variation must be minimized. One of the methods proposed extracts local descriptors in the image blocks and provides an estimate of change using a non-parametric model (KDE). Unlike background subtraction and remote sensing methods which are based on pixels and assume independence between them, the proposed approach not requires a complex learning phase and it is capable of detecting changes using only two images. The second method applies an image segmentation before make the matching of the similar regions. In the experiments, the proposed approaches are compared to techniques used in change detection. According to the results, the proposed approach based on non-parametric model outperforms other methods found in the literature, mainly due to the fact that the approach is more robust to lighting variation. The results also demonstrate that the approach is able to filter images that should be further analyzed by operators.}, keywords = {Change Detection}, pubstate = {published}, tppubtype = {phdthesis} } This study addresses the problem of change detection in landscapes using aerial images acquired at different times, important for many applications. The monitoring pipeline, for instance, the usual way to change detection is a task performed by human operators which evaluates a video of a monitoring camera and searches for changes in the scene from the comparison of image pairs. This procedure is prone to errors because it is a tedious task, therefore is a justification to automated method. Besides enabling the reduction of errors and speed up the monitoring process, a system automatic can be used as a filter to provide a set of key frames that should receive more attention from the operator. Thus, the system can help the operators on the decision making process regarding the actions to be performed. The basic procedure for detecting changes is to find a set of pixels or regions that are different in another test image. However, images acquired at different dates may be influenced by radiometric and registration factors. In other words, the influence of camera movement, lighting variation, and atmospheric variation must be minimized. One of the methods proposed extracts local descriptors in the image blocks and provides an estimate of change using a non-parametric model (KDE). Unlike background subtraction and remote sensing methods which are based on pixels and assume independence between them, the proposed approach not requires a complex learning phase and it is capable of detecting changes using only two images. The second method applies an image segmentation before make the matching of the similar regions. In the experiments, the proposed approaches are compared to techniques used in change detection. According to the results, the proposed approach based on non-parametric model outperforms other methods found in the literature, mainly due to the fact that the approach is more robust to lighting variation. The results also demonstrate that the approach is able to filter images that should be further analyzed by operators. |
dos Junior, Cassio Elias Santos; Kijak, Ewa; Gravier, Guillaume; Schwartz, William Robson Partial least squares for face hashing Journal Article Neurocomputing, 213 , pp. 34–47, 2016. Links | BibTeX | Tags: Face Identification, Face Recognition, Featured Publication, Partial Least Squares @article{Santos:2016:Neurocomputing, title = {Partial least squares for face hashing}, author = {Cassio Elias Santos dos Junior and Ewa Kijak and Guillaume Gravier and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_Neurocomputing_Santos.pdf}, year = {2016}, date = {2016-02-03}, journal = {Neurocomputing}, volume = {213}, pages = {34--47}, keywords = {Face Identification, Face Recognition, Featured Publication, Partial Least Squares}, pubstate = {published}, tppubtype = {article} } |
Junior, Antonio Carlos Nazare; Schwartz, William Robson A scalable and flexible framework for smart video surveillance Journal Article Computer Vision and Image Understanding, 144 (C), pp. 258–275, 2016. Links | BibTeX | Tags: Smart Surveillance, Smart Surveillance Framework, SSF, Surveillance Systems, VER+, Video Surveillance @article{Nazare:2016:CVIU, title = {A scalable and flexible framework for smart video surveillance}, author = {Antonio Carlos Nazare Junior and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_CVIU.pdf}, year = {2016}, date = {2016-01-01}, journal = {Computer Vision and Image Understanding}, volume = {144}, number = {C}, pages = {258--275}, keywords = {Smart Surveillance, Smart Surveillance Framework, SSF, Surveillance Systems, VER+, Video Surveillance}, pubstate = {published}, tppubtype = {article} } |
Rodrigues, Marco Tulio Alves; de Mesquita, Daniel Balbino; Nascimento, Erickson R; Schwartz, William Robson Change detection based on feature invariant to monotonic transforms and spatially constrained matching Journal Article Journal of Electronic Imaging, 25 (1), pp. 1-10, 2016. Links | BibTeX | Tags: Change Detection @article{Rodrigues:2016:JEI, title = {Change detection based on feature invariant to monotonic transforms and spatially constrained matching}, author = {Marco Tulio Alves Rodrigues and Daniel Balbino de Mesquita and Erickson R Nascimento and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_JEI.pdf}, year = {2016}, date = {2016-01-01}, journal = {Journal of Electronic Imaging}, volume = {25}, number = {1}, pages = {1-10}, keywords = {Change Detection}, pubstate = {published}, tppubtype = {article} } |
2019 |
Artur Jordao; Ricardo Kloss; Fernando Yamada; William Robson Schwartz Pruning Deep Networks using Partial Least Squares Inproceedings British Machine Vision Conference (BMVC) Workshops, pp. 1-9, 2019. @inproceedings{Jordao:2019:BMVC, title = {Pruning Deep Networks using Partial Least Squares}, author = {Artur Jordao and Ricardo Kloss and Fernando Yamada and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_BMVCW_Jordao.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {British Machine Vision Conference (BMVC) Workshops}, pages = {1-9}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Raphael Felipe Carvalho de Prates; William Robson Schwartz Kernel cross-view collaborative representation based classification for person re-identification Journal Article Journal of Visual Communication and Image Representation, 58 (1), pp. 304-315, 2019. Links | BibTeX | Tags: Kernel collaborative representation based classification, Person Re-Identification @article{Prates:2019:JVCI, title = {Kernel cross-view collaborative representation based classification for person re-identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, doi = {https://doi.org/10.1016/j.jvcir.2018.12.003}, year = {2019}, date = {2019-01-01}, journal = {Journal of Visual Communication and Image Representation}, volume = {58}, number = {1}, pages = {304-315}, keywords = {Kernel collaborative representation based classification, Person Re-Identification}, pubstate = {published}, tppubtype = {article} } |
Carlos Caetano; Victor H C de Melo; François Brémond; Jefersson A dos Santos; William Robson Schwartz Magnitude-Orientation Stream network and depth information applied to activity recognition Journal Article Journal of Visual Communication and Image Representation, 63 , pp. 102596, 2019, ISSN: 1047-3203. Resumo | Links | BibTeX | Tags: @article{CAETANO2019102596, title = {Magnitude-Orientation Stream network and depth information applied to activity recognition}, author = {Carlos Caetano and Victor H C de Melo and François Brémond and Jefersson A dos Santos and William Robson Schwartz}, url = {http://www.sciencedirect.com/science/article/pii/S1047320319302172}, doi = {https://doi.org/10.1016/j.jvcir.2019.102596}, issn = {1047-3203}, year = {2019}, date = {2019-01-01}, journal = {Journal of Visual Communication and Image Representation}, volume = {63}, pages = {102596}, abstract = {The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Moreover, we also employ depth information to use as a weighting scheme on the magnitude information to compensate the distance of the subjects performing the activity to the camera. Experimental results, carried on two well-known datasets (UCF101 and NTU), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Moreover, we also employ depth information to use as a weighting scheme on the magnitude information to compensate the distance of the subjects performing the activity to the camera. Experimental results, carried on two well-known datasets (UCF101 and NTU), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation. |
Gabriel Resende Goncalves; Matheus Alves Diniz; Rayson Laroca; David Menotti; William Robson Schwartz Multi-Task Learning for Low-Resolution License Plate Recognition Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 1-10, 2019. @inproceedings{Goncalves:2019:CIARP, title = {Multi-Task Learning for Low-Resolution License Plate Recognition}, author = {Gabriel Resende Goncalves and Matheus Alves Diniz and Rayson Laroca and David Menotti and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_CIARP_Goncalves.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, pages = {1-10}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Rafael Henrique Vareto; Matheus Alves Diniz; William Robson Schwartz Face Spoofing Detection on Low-Power Devices using Embeddings with Spatial and Frequency-based Descriptors Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 1-10, 2019. @inproceedings{Vareto:2019:CIARP, title = {Face Spoofing Detection on Low-Power Devices using Embeddings with Spatial and Frequency-based Descriptors}, author = {Rafael Henrique Vareto and Matheus Alves Diniz and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_CIARP_Vareto.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, pages = {1-10}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Vitor Cezar de Lima; William Robson Schwartz Gait Recognition using Pose Estimation and Signal Processing Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 1-10, 2019. @inproceedings{Lima:2019:CIARP, title = {Gait Recognition using Pose Estimation and Signal Processing}, author = {Vitor Cezar de Lima and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_CIARP_Vitor.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, pages = {1-10}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Igor Bastos; Victor Hugo Cunha de Melo; William Robson Schwartz Multi-Loss Recurrent Residual Networks for Gesture Detection and Recognition Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2019. @inproceedings{Bastos:2019:SIBGRAPIb, title = {Multi-Loss Recurrent Residual Networks for Gesture Detection and Recognition}, author = {Igor Bastos and Victor Hugo Cunha de Melo and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_SIBGRAPI_Bastos.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
G Mendes; Jose Gustavo Paiva; William Robson Schwartz Point-placement techniques and Temporal Self-similarity Maps for Visual Analysis of Surveillance Videos Inproceedings International Conference Information Visualisation, pp. 1-8, 2019. BibTeX | Tags: @inproceedings{Mendes:2019:ICIV, title = {Point-placement techniques and Temporal Self-similarity Maps for Visual Analysis of Surveillance Videos}, author = {G Mendes and Jose Gustavo Paiva and William Robson Schwartz}, year = {2019}, date = {2019-01-01}, booktitle = {International Conference Information Visualisation}, pages = {1-8}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Carlos Caetano; Francois Bremond; William Robson Schwartz Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2019. @inproceedings{Caetano:2019:SIBGRAPIb, title = {Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints}, author = {Carlos Caetano and Francois Bremond and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_SIBGRAPI_Caetano.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Carlos Caetano; Jessica Souza; Francois Bremond; Jefersson Santos; William Robson Schwartz SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition Inproceedings 16th International Conference on Advanced Video and Signal-based Surveillance (AVSS), pp. 1-6, 2019. @inproceedings{Caetano:2019:AVSSb, title = {SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition}, author = {Carlos Caetano and Jessica Souza and Francois Bremond and Jefersson Santos and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_AVSS_Caetano.pdf}, year = {2019}, date = {2019-01-01}, booktitle = {16th International Conference on Advanced Video and Signal-based Surveillance (AVSS)}, pages = {1-6}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2018 |
Victor Hugo Cunha de Melo; Jesimon Barreto Santos; Carlos Antonio Caetano Junior; Jessica Sena; Otavio A B Penatti; William Robson Schwartz Object-based Temporal Segment Relational Network for Activity Recognition Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. BibTeX | Tags: Activity Recognition, DeepEyes, HAR-HEALTH, Relational Reasoning, Spatial Pyramid @inproceedings{DeMelo:2018:SIBGRAPI, title = {Object-based Temporal Segment Relational Network for Activity Recognition}, author = {Victor Hugo Cunha de Melo and Jesimon Barreto Santos and Carlos Antonio Caetano Junior and Jessica Sena and Otavio A B Penatti and William Robson Schwartz}, year = {2018}, date = {2018-09-21}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Activity Recognition, DeepEyes, HAR-HEALTH, Relational Reasoning, Spatial Pyramid}, pubstate = {published}, tppubtype = {inproceedings} } |
Jessica Sena; Jesimon Barreto Santos; William Robson Schwartz Multiscale DCNN Ensemble Applied to Human Activity Recognition Based on Wearable Sensors Inproceedings 26th European Signal Processing Conference (EUSIPCO 2018), pp. 1-5, 2018. Links | BibTeX | Tags: Activity Recognition Based on Wearable Sensors, Deep Learning, DeepEyes, HAR-HEALTH, Human Activity Recognition, Multimodal Data, Wearable Sensors @inproceedings{Sena:2018:EUSIPCO, title = {Multiscale DCNN Ensemble Applied to Human Activity Recognition Based on Wearable Sensors}, author = {Jessica Sena and Jesimon Barreto Santos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/PID5428933.pdf}, year = {2018}, date = {2018-09-06}, booktitle = {26th European Signal Processing Conference (EUSIPCO 2018)}, pages = {1-5}, keywords = {Activity Recognition Based on Wearable Sensors, Deep Learning, DeepEyes, HAR-HEALTH, Human Activity Recognition, Multimodal Data, Wearable Sensors}, pubstate = {published}, tppubtype = {inproceedings} } |
Gabriel Resende Gonçalves; Matheus Alves Diniz; Rayson Laroca; David Menotti; William Robson Schwartz Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. Links | BibTeX | Tags: Automatic License Plate Recognition, Deep Learning, DeepEyes, GigaFrames, Multi-Task Learning, Sense-ALPR @inproceedings{Goncalves:2018:SIBGRAPI, title = {Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks}, author = {Gabriel Resende Gonçalves and Matheus Alves Diniz and Rayson Laroca and David Menotti and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper.pdf}, year = {2018}, date = {2018-09-04}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Automatic License Plate Recognition, Deep Learning, DeepEyes, GigaFrames, Multi-Task Learning, Sense-ALPR}, pubstate = {published}, tppubtype = {inproceedings} } |
Raphael Felipe Carvalho de Prates; William Robson Schwartz Kernel multiblock partial least squares for a scalable and multicamera person reidentification system Journal Article pp. 1-33, 2018. Links | BibTeX | Tags: Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification @article{Prates:2018:JEI, title = {Kernel multiblock partial least squares for a scalable and multicamera person reidentification system}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/article.pdf}, year = {2018}, date = {2018-06-25}, booktitle = {Journal of Electronic Imaging}, pages = {1-33}, keywords = {Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {article} } |
Carlos Antonio Caetano Junior; Jefersson A dos Santos; William Robson Schwartz Statistical Measures from Co-occurrence of Codewords for Action Recognition Inproceedings VISAPP 2018 - International Conference on Computer Vision Theory and Applications, pp. 1-8, 2018. Links | BibTeX | Tags: Action Recognition, Activity Recognition, DeepEyes, GigaFrames, Spatiotemporal Features @inproceedings{Caetano:2018:VISAPP, title = {Statistical Measures from Co-occurrence of Codewords for Action Recognition}, author = {Carlos Antonio Caetano Junior and Jefersson A dos Santos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/VISAPP_2018_CarlosCaetano.pdf}, year = {2018}, date = {2018-01-27}, booktitle = {VISAPP 2018 - International Conference on Computer Vision Theory and Applications}, pages = {1-8}, keywords = {Action Recognition, Activity Recognition, DeepEyes, GigaFrames, Spatiotemporal Features}, pubstate = {published}, tppubtype = {inproceedings} } |
R Laroca; E Severo; L A Zanlorensi; L S Oliveira; G R Gon�alves; W R Schwartz; D Menotti A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector Inproceedings 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-10, 2018. @inproceedings{Laroca:2018:IJCNN, title = {A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector}, author = {R Laroca and E Severo and L A Zanlorensi and L S Oliveira and G R Gon�alves and W R Schwartz and D Menotti}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2018_IJCNN_Laroca.pdf}, doi = {10.1109/IJCNN.2018.8489629}, year = {2018}, date = {2018-01-01}, booktitle = {2018 International Joint Conference on Neural Networks (IJCNN)}, pages = {1-10}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Gabriel Sousa Ferreira; Flavio Luis Cardeal Padua; William Robson Schwartz; Marco Tulio Alves Nolasco Rodrigues Mapping Sports Interest with Social Network Inproceedings XV Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC), pp. 1-12, 2018. @inproceedings{Ferreira:2018:ENIAC, title = {Mapping Sports Interest with Social Network}, author = {Gabriel Sousa Ferreira and Flavio Luis Cardeal Padua and William Robson Schwartz and Marco Tulio Alves Nolasco Rodrigues}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2018_ENIAC.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {XV Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC)}, pages = {1-12}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ricardo Barbosa Kloss; Artur Jordao; William Robson Schwartz Face Verification Strategies for Employing Deep Models Inproceedings 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 258-262, 2018. Links | BibTeX | Tags: Artificial Neural Networks, Face Verification, GigaFrames, Metric Learning, Transfer Learning @inproceedings{Kloss:2018:FG, title = {Face Verification Strategies for Employing Deep Models}, author = {Ricardo Barbosa Kloss and Artur Jordao and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Face-Verification-Strategies-for-Employing-Deep-Models.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {13th IEEE International Conference on Automatic Face & Gesture Recognition}, pages = {258-262}, keywords = {Artificial Neural Networks, Face Verification, GigaFrames, Metric Learning, Transfer Learning}, pubstate = {published}, tppubtype = {inproceedings} } |
Artur Jordao; Antonio Carlos Nazare Junior; Jessica Sena; William Robson Schwartz Human Activity Recognition based on Wearable Sensor Data: A Benchmark Journal Article arXiv, pp. 1-12, 2018. Links | BibTeX | Tags: Activity Recognition Based on Wearable Sensors, Benchmark on Activity Recognition based on Wearable Sensors, Wearable Sensors @article{Jordao:2018:arXiv, title = {Human Activity Recognition based on Wearable Sensor Data: A Benchmark}, author = {Artur Jordao and Antonio Carlos Nazare Junior and Jessica Sena and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/Human-Activity-Recognition-based-on-Wearable-A-Benchmark.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {Arvix}, journal = {arXiv}, pages = {1-12}, keywords = {Activity Recognition Based on Wearable Sensors, Benchmark on Activity Recognition based on Wearable Sensors, Wearable Sensors}, pubstate = {published}, tppubtype = {article} } |
Artur Jordao; Ricardo Barbosa Kloss; William Robson Schwartz Latent hypernet: Exploring all Layers from Convolutional Neural Networks Inproceedings IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2018. Links | BibTeX | Tags: Activity Recognition Based on Wearable Sensors, DeepEyes, GigaFrames, Partial Least Squares, Wearable Sensors @inproceedings{Jordao:2018b:IJCNN, title = {Latent hypernet: Exploring all Layers from Convolutional Neural Networks}, author = {Artur Jordao and Ricardo Barbosa Kloss and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Latent-HyperNet-Exploring-the-Layers.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {IEEE International Joint Conference on Neural Networks (IJCNN)}, pages = {1-7}, keywords = {Activity Recognition Based on Wearable Sensors, DeepEyes, GigaFrames, Partial Least Squares, Wearable Sensors}, pubstate = {published}, tppubtype = {inproceedings} } |
Artur Jordao; Leonardo Antônio Borges Torres; William Robson Schwartz Novel Approaches to Human Activity Recognition based on Accelerometer Data Journal Article 12 (7), pp. 1387–1394, 2018. Links | BibTeX | Tags: Activity Recognition Based on Wearable Sensors, HAR-HEALTH, Wearable Sensors @article{Jordao:2018:SIVP, title = {Novel Approaches to Human Activity Recognition based on Accelerometer Data}, author = {Artur Jordao and Leonardo Antônio Borges Torres and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Novel-Approaches-to-Human-Activity-Recognition-based-on.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {Signal, Image and Video Processing}, volume = {12}, number = {7}, pages = {1387–1394}, keywords = {Activity Recognition Based on Wearable Sensors, HAR-HEALTH, Wearable Sensors}, pubstate = {published}, tppubtype = {article} } |
Rensso Victor Hugo Mora Colque; Carlos Antonio Caetano Junior; Victor Hugo Cunha de Melo; Guillermo Camara Chavez; William Robson Schwartz Novel Anomalous Event Detection based on Human-object Interactions Inproceedings VISAPP 2018 - International Conference on Computer Vision Theory and Applications, pp. 1-8, 2018. Links | BibTeX | Tags: Anomalous Event Detection, Contextual Information, DeepEyes, GigaFrames, Human-Object Interaction @inproceedings{Colque:2018:VISAPP, title = {Novel Anomalous Event Detection based on Human-object Interactions}, author = {Rensso Victor Hugo Mora Colque and Carlos Antonio Caetano Junior and Victor Hugo Cunha de Melo and Guillermo Camara Chavez and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/VISAPP_2018_92.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {VISAPP 2018 - International Conference on Computer Vision Theory and Applications}, pages = {1-8}, keywords = {Anomalous Event Detection, Contextual Information, DeepEyes, GigaFrames, Human-Object Interaction}, pubstate = {published}, tppubtype = {inproceedings} } |
Igor Leonardo Oliveira Bastos; Victor Hugo Cunha de Melo; Gabriel Resende Gonçalves; William Robson Schwartz MORA: A Generative Approach to Extract Spatiotemporal Information Applied to Gesture Recognition Inproceedings 15th International Conference on Advanced Video and Signal-based Surveillance (AVSS), pp. 1-6, 2018. Links | BibTeX | Tags: Autoencoders, DeepEyes, Gesture Recognition, GigaFrames, Recurrent Models @inproceedings{Bastos:2018:AVSS, title = {MORA: A Generative Approach to Extract Spatiotemporal Information Applied to Gesture Recognition}, author = {Igor Leonardo Oliveira Bastos and Victor Hugo Cunha de Melo and Gabriel Resende Gonçalves and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/MORA_.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {15th International Conference on Advanced Video and Signal-based Surveillance (AVSS)}, pages = {1-6}, keywords = {Autoencoders, DeepEyes, Gesture Recognition, GigaFrames, Recurrent Models}, pubstate = {published}, tppubtype = {inproceedings} } |
Ricardo Barbosa Kloss; Artur Jordao; William Robson Schwartz Boosted Projection An Ensemble of Transformation Models Inproceedings 22nd Iberoamerican Congress on Pattern Recognition (CIARP), pp. 331-338, 2018. Links | BibTeX | Tags: Computer vision, DeepEyes, Dimensionality Reduction, Ensemble Partial Least Squares, GigaFrames, Machine Learning @inproceedings{Kloss:2018:CIARP, title = {Boosted Projection An Ensemble of Transformation Models}, author = {Ricardo Barbosa Kloss and Artur Jordao and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Boosted-Projection-An-Ensemble-of-Transformation-Models.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {22nd Iberoamerican Congress on Pattern Recognition (CIARP)}, pages = {331-338}, keywords = {Computer vision, DeepEyes, Dimensionality Reduction, Ensemble Partial Least Squares, GigaFrames, Machine Learning}, pubstate = {published}, tppubtype = {inproceedings} } |
Renan Oliveira Reis; Igor Henrique Dias; William Robson Schwartz Neural network control for active cameras using master-slave setup Inproceedings International Conference on Advanced Video and Signal-based Surveillance (AVSS), pp. 1-6, 2018. Links | BibTeX | Tags: Active Camera, DeepEyes, GigaFrames, Neural network control for active cameras using master-slave setup, SMS @inproceedings{Reis:2018:AVSS, title = {Neural network control for active cameras using master-slave setup}, author = {Renan Oliveira Reis and Igor Henrique Dias and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/renan_avss_2018-1-1.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {International Conference on Advanced Video and Signal-based Surveillance (AVSS)}, pages = {1-6}, keywords = {Active Camera, DeepEyes, GigaFrames, Neural network control for active cameras using master-slave setup, SMS}, pubstate = {published}, tppubtype = {inproceedings} } |
Antonio Carlos Nazare Junior; Filipe Oliveira de Costa; William Robson Schwartz Content-Based Multi-Camera Video Alignment using Accelerometer Data Inproceedings Advanced Video and Signal Based Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 1-6, 2018. Links | BibTeX | Tags: Camera Synchronization, DeepEyes, GigaFrames, SensorCap, Sensors, SMS @inproceedings{Nazare:2018:AVSS, title = {Content-Based Multi-Camera Video Alignment using Accelerometer Data}, author = {Antonio Carlos Nazare Junior and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2018_avss_svsync_camera_ready.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {Advanced Video and Signal Based Surveillance (AVSS), 2018 15th IEEE International Conference on}, pages = {1-6}, keywords = {Camera Synchronization, DeepEyes, GigaFrames, SensorCap, Sensors, SMS}, pubstate = {published}, tppubtype = {inproceedings} } |
Artur Jordao; Ricardo Kloss; Fernando Yamada; William Robson Schwartz Pruning Deep Neural Networks using Partial Least Squares Journal Article ArXiv e-prints, 2018. Links | BibTeX | Tags: DeepEyes, GigaFrames, Neural Networks Optimization @article{Jordao:2018:arXivb, title = {Pruning Deep Neural Networks using Partial Least Squares}, author = {Artur Jordao and Ricardo Kloss and Fernando Yamada and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/1810.07610.pdf}, year = {2018}, date = {2018-01-01}, journal = {ArXiv e-prints}, keywords = {DeepEyes, GigaFrames, Neural Networks Optimization}, pubstate = {published}, tppubtype = {article} } |
Raphael Prates; William Robson Schwartz Kernel Multiblock Partial Least Squares for a Scalable and Multicamera Person Reidentification System Journal Article Journal of Electronic Imaging, 27 (3), pp. 1-33, 2018. @article{Prates:2018:JEIb, title = {Kernel Multiblock Partial Least Squares for a Scalable and Multicamera Person Reidentification System}, author = {Raphael Prates and William Robson Schwartz}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2018_JEI.pdf}, doi = {10.1117/1.JEI.27.3.033041}, year = {2018}, date = {2018-01-01}, journal = {Journal of Electronic Imaging}, volume = {27}, number = {3}, pages = {1-33}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Anderson Luis Cavalcanti Sales; Rafael Henrique Vareto; William Robson Schwartz; Guillermo Camara Chavez Single-Shot Person Re-Identification Combining Similarity Metrics and Support Vectors Inproceedings Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. @inproceedings{Sales:2018:SIBGRAPI, title = {Single-Shot Person Re-Identification Combining Similarity Metrics and Support Vectors}, author = {Anderson Luis Cavalcanti Sales and Rafael Henrique Vareto and William Robson Schwartz and Guillermo Camara Chavez}, url = {http://www.dcc.ufmg.br/~william/papers/paper_2018_SIBGRAPI_Sales.pdf}, year = {2018}, date = {2018-01-01}, booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2017 |
Igor Leonardo Oliveira Bastos; Larissa Rocha Soares; William Robson Schwartz Pyramidal Zernike Over Time: A spatiotemporal feature descriptor based on Zernike Moments Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP 2017), pp. 77-85, 2017. Links | BibTeX | Tags: Activity Recognition, DeepEyes, Feature Extraction, GigaFrames, Zernike Moments @inproceedings{Bastos:2017:CIARP, title = {Pyramidal Zernike Over Time: A spatiotemporal feature descriptor based on Zernike Moments}, author = {Igor Leonardo Oliveira Bastos and Larissa Rocha Soares and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/PZOT_camera_ready.pdf}, year = {2017}, date = {2017-11-07}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP 2017)}, pages = {77-85}, keywords = {Activity Recognition, DeepEyes, Feature Extraction, GigaFrames, Zernike Moments}, pubstate = {published}, tppubtype = {inproceedings} } |
Rafael Henrique Vareto; Samira Santos da Silva; Filipe Oliveira de Costa; William Robson Schwartz Towards Open-Set Face Recognition using Hashing Functions Inproceedings International Joint Conference on Biometrics (IJCB), pp. 1-8, 2017. Links | BibTeX | Tags: Face Recognition, Open-Set Classification @inproceedings{Vareto:2017:IJCB, title = {Towards Open-Set Face Recognition using Hashing Functions}, author = {Rafael Henrique Vareto and Samira Santos da Silva and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_IJCB.pdf http://lena.dcc.ufmg.br/wordpress/towards-open-set-face-recognition-using-hashing-functions/}, year = {2017}, date = {2017-01-01}, booktitle = {International Joint Conference on Biometrics (IJCB)}, pages = {1-8}, keywords = {Face Recognition, Open-Set Classification}, pubstate = {published}, tppubtype = {inproceedings} } |
Rensso Victor Hugo Mora Colque; Carlos Antonio Caetano Junior; Matheus Toledo Lustosa de Andrade; William Robson Schwartz Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos Journal Article IEEE Transactions on Circuits and Systems for Video Technology, 27 (3), pp. 673-682, 2017. Links | BibTeX | Tags: Anomalous Event Detection, DeepEyes, Feature Extraction, GigaFrames @article{Colque:2016:TCSVT, title = {Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos}, author = {Rensso Victor Hugo Mora Colque and Carlos Antonio Caetano Junior and Matheus Toledo Lustosa de Andrade and William Robson Schwartz}, url = {http://dx.doi.org/10.1109/TCSVT.2016.2637778}, year = {2017}, date = {2017-01-01}, journal = {IEEE Transactions on Circuits and Systems for Video Technology}, volume = {27}, number = {3}, pages = {673-682}, keywords = {Anomalous Event Detection, DeepEyes, Feature Extraction, GigaFrames}, pubstate = {published}, tppubtype = {article} } |
Igor Leonardo Oliveira Bastos; William Robson Schwartz Assigning Relative Importance to Scene Elements Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2017. Links | BibTeX | Tags: Assigning Relative Importance to Scene Elements, Importance Annotation for VIP and UIUC Pascal Sentence Datasets, Importance Assignment @inproceedings{Bastos:2017:SIBGRAPI, title = {Assigning Relative Importance to Scene Elements}, author = {Igor Leonardo Oliveira Bastos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_SIBGRAPI_Bastos.pdf}, year = {2017}, date = {2017-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Assigning Relative Importance to Scene Elements, Importance Annotation for VIP and UIUC Pascal Sentence Datasets, Importance Assignment}, pubstate = {published}, tppubtype = {inproceedings} } |
Carlos Antonio Caetano Junior; Victor Hugo Cunha de Melo; Jefersson Alex dos Santos; William Robson Schwartz Activity Recognition based on a Magnitude-Orientation Stream Network Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2017. Links | BibTeX | Tags: Activity Recognition, Deep Learning, GigaFrames @inproceedings{Caetano:2017:SIBGRAPI, title = {Activity Recognition based on a Magnitude-Orientation Stream Network}, author = {Carlos Antonio Caetano Junior and Victor Hugo Cunha de Melo and Jefersson Alex dos Santos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_SIBGRAPI_Caetano.pdf}, year = {2017}, date = {2017-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Activity Recognition, Deep Learning, GigaFrames}, pubstate = {published}, tppubtype = {inproceedings} } |
Rafael Henrique Vareto; Samira Santos da Silva; Filipe Oliveira de Costa; William Robson Schwartz Face Verification based on Relational Disparity Features and Partial Least Squares Models Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2017. Links | BibTeX | Tags: Face Recognition, Face Verification @inproceedings{Vareto:2017:SIBGRAPI, title = {Face Verification based on Relational Disparity Features and Partial Least Squares Models}, author = {Rafael Henrique Vareto and Samira Santos da Silva and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_SIBGRAPI_Vareto.pdf http://lena.dcc.ufmg.br/wordpress/face-verification-based-relational-disparity-features-partial-least-squares-models/}, year = {2017}, date = {2017-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {1-8}, keywords = {Face Recognition, Face Verification}, pubstate = {published}, tppubtype = {inproceedings} } |
2016 |
Raphael Felipe Carvalho de Prates; William Robson Schwartz Kernel Hierarchical PCA for Person Re-Identification Inproceedings IAPR International Conference on Pattern Recognition (ICPR), 2016. Resumo | Links | BibTeX | Tags: Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification @inproceedings{Prates2016ICPR, title = {Kernel Hierarchical PCA for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Kernel_HPCA_final.pdf}, year = {2016}, date = {2016-12-13}, booktitle = {IAPR International Conference on Pattern Recognition (ICPR)}, abstract = {Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person ReID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions.}, keywords = {Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person ReID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions. |
Artur Jordao; Jessica Sena; William Robson Schwartz A Late Fusion Approach to Combine Multiple Pedestrian Detectors Inproceedings IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016. Links | BibTeX | Tags: DeepEyes, Featured Publication, GigaFrames, Pedestrian Detection @inproceedings{Correia:2016:ICPR, title = {A Late Fusion Approach to Combine Multiple Pedestrian Detectors}, author = {Artur Jordao and Jessica Sena and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/A-Late-Fusion-Approach-to-Combine-Multiple.pdf}, year = {2016}, date = {2016-12-13}, booktitle = {IAPR International Conference on Pattern Recognition (ICPR)}, pages = {1-6}, keywords = {DeepEyes, Featured Publication, GigaFrames, Pedestrian Detection}, pubstate = {published}, tppubtype = {inproceedings} } |
Carlos Antonio Caetano Junior; Jefersson A dos Santos; William Robson Schwartz Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor Inproceedings IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016. Links | BibTeX | Tags: Activity Recognition, Descriptor, Feature Extraction, Featured Publication, OFCM, VER+ @inproceedings{Caetano:2016:ICPR, title = {Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor}, author = {Carlos Antonio Caetano Junior and Jefersson A dos Santos and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/ICPR2016.pdf}, year = {2016}, date = {2016-12-13}, booktitle = {IAPR International Conference on Pattern Recognition (ICPR)}, pages = {1-6}, keywords = {Activity Recognition, Descriptor, Feature Extraction, Featured Publication, OFCM, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Gabriel Resende Gonçalves; David Menotti; William Robson Schwartz License Plate Recognition based on Temporal Redundancy Inproceedings IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1-5, 2016. Links | BibTeX | Tags: Automatic License Plate Recognition, DeepEyes, GigaFrames @inproceedings{Goncalves:2016:ITSC, title = {License Plate Recognition based on Temporal Redundancy}, author = {Gabriel Resende Gonçalves and David Menotti and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_ITSC.pdf}, year = {2016}, date = {2016-11-04}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, pages = {1-5}, keywords = {Automatic License Plate Recognition, DeepEyes, GigaFrames}, pubstate = {published}, tppubtype = {inproceedings} } |
Gabriel Resende Gonçalves; Sirlene Pio Gomes da Silva; David Menotti; William Robson Schwartz Benchmark for License Plate Character Segmentation Journal Article Journal of Electronic Imaging, 25 (5), pp. 1-5, 2016, ISBN: 1017-9909. Links | BibTeX | Tags: Automatic License Plate Recognition, Benchmark, Character Segmentation, DeepEyes, Featured Publication, GigaFrames, Jaccard Coefficient, Novel Dataset, Sense SegPlate @article{2016:JEI:Gabriel, title = {Benchmark for License Plate Character Segmentation}, author = {Gabriel Resende Gonçalves and Sirlene Pio Gomes da Silva and David Menotti and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/JEI-2016-Benchmark.pdf}, isbn = {1017-9909}, year = {2016}, date = {2016-10-24}, journal = {Journal of Electronic Imaging}, volume = {25}, number = {5}, pages = {1-5}, keywords = {Automatic License Plate Recognition, Benchmark, Character Segmentation, DeepEyes, Featured Publication, GigaFrames, Jaccard Coefficient, Novel Dataset, Sense SegPlate}, pubstate = {published}, tppubtype = {article} } |
Rafael Henrique Vareto; Filipe Oliveira de Costa; William Robson Schwartz Face Identification in Large Galleries Inproceedings Workshop on Face Processing Applications, pp. 1-4, 2016. Links | BibTeX | Tags: DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+ @inproceedings{Vareto:2016:WFPA, title = {Face Identification in Large Galleries}, author = {Rafael Henrique Vareto and Filipe Oliveira de Costa and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_WFPA.pdf}, year = {2016}, date = {2016-10-02}, booktitle = {Workshop on Face Processing Applications}, pages = {1-4}, keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Raphael Felipe Carvalho de Prates; Cristianne Rodrigues Santos Dutra; William Robson Schwartz Predominant Color Name Indexing Structure for Person Re-Identification Inproceedings IEEE International Conference on Image Processing (ICIP), 2016. Resumo | Links | BibTeX | Tags: GigaFrames, Person Re-Identification, VER+ @inproceedings{Prates2016ICIP, title = {Predominant Color Name Indexing Structure for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and Cristianne Rodrigues Santos Dutra and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_ICIP_Prates.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, abstract = {The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery.}, keywords = {GigaFrames, Person Re-Identification, VER+}, pubstate = {published}, tppubtype = {inproceedings} } The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery. |
Raphael Felipe Carvalho de Prates; Marina Santos Oliveira; William Robson Schwartz Kernel Partial Least Squares for Person Re-Identification Inproceedings IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016. Resumo | Links | BibTeX | Tags: DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification @inproceedings{Prates2016AVSS, title = {Kernel Partial Least Squares for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and Marina Santos Oliveira and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/egpaper_for_DoubleBlindReview.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)}, abstract = {Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S.}, keywords = {DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S. |
Artur Jordao; William Robson Schwartz Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection Inproceedings IEEE International Conference on Image Processing (ICIP), pp. 2931-2935, 2016. Links | BibTeX | Tags: DeepEyes, Pedestrian Detection, VER+ @inproceedings{Correia:2016:ICIP, title = {Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection}, author = {Artur Jordao and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/OBLIQUE-RANDOM-FOREST-BASED-ON-PARTIAL-LEAST-SQUARES-APPLIED-TO.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, pages = {2931-2935}, keywords = {DeepEyes, Pedestrian Detection, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Gabriel Resende Goncalves License Plate Recognition based on Temporal Redundancy Masters Thesis Federal University of Minas Gerais, 2016. Resumo | Links | BibTeX | Tags: Automatic License Plate Recognition, DeepEyes, GigaFrames @mastersthesis{Goncalves:2016:MSc, title = {License Plate Recognition based on Temporal Redundancy}, author = {Gabriel Resende Goncalves}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_Gabriel.pdf}, year = {2016}, date = {2016-08-26}, school = {Federal University of Minas Gerais}, abstract = {Recognition of vehicle license plates is an important task applied to a myriad of real scenarios. Most approaches in the literature first detect an on-track vehicle, locate the license plate, perform a segmentation of its characters and then recognize the characters using an Optical Character Recognition (OCR) approach. However, these approaches focus on performing these tasks using only a single frame of each vehicle in the video. Therefore, such techniques might have their recognition rates reduced due to noise present in that particular frame. On the other hand, in this work we propose an approach to automatically detect the vehicle on the road and identify (locate/recognize) its license plate based on temporal redundant information instead of selecting a single frame to perform the recognition. We also propose two post-processing steps that can be employed to improve the accuracy of the system by querying a license plate database (e.g., the Department of Motor Vehicles database containing a list of all issued license plates and car models). Experimental results demonstrate that it is possible to improve the vehicle recognition rate in 15.5 percentage points (p.p.) (an increase of 23.38%) of the baseline results, using our proposal temporal redundancy approach. Furthermore, additional 7.8 p.p. are achieved using the two post-processing approaches, leading to a final recognition rate of 89.6% on a dataset with 5,200 frame images of $300$ vehicles recorded at Federal University of Minas Gerais (UFMG). In addition, this work also proposes a novel benchmark, designed specifically to evaluate character segmentation techniques, composed of a dataset of 2,000 Brazilian license plates (resulting in 14,000 alphanumeric symbols) and an evaluation protocol considering a novel evaluation measure, the Jaccard-Centroid coefficient.}, keywords = {Automatic License Plate Recognition, DeepEyes, GigaFrames}, pubstate = {published}, tppubtype = {mastersthesis} } Recognition of vehicle license plates is an important task applied to a myriad of real scenarios. Most approaches in the literature first detect an on-track vehicle, locate the license plate, perform a segmentation of its characters and then recognize the characters using an Optical Character Recognition (OCR) approach. However, these approaches focus on performing these tasks using only a single frame of each vehicle in the video. Therefore, such techniques might have their recognition rates reduced due to noise present in that particular frame. On the other hand, in this work we propose an approach to automatically detect the vehicle on the road and identify (locate/recognize) its license plate based on temporal redundant information instead of selecting a single frame to perform the recognition. We also propose two post-processing steps that can be employed to improve the accuracy of the system by querying a license plate database (e.g., the Department of Motor Vehicles database containing a list of all issued license plates and car models). Experimental results demonstrate that it is possible to improve the vehicle recognition rate in 15.5 percentage points (p.p.) (an increase of 23.38%) of the baseline results, using our proposal temporal redundancy approach. Furthermore, additional 7.8 p.p. are achieved using the two post-processing approaches, leading to a final recognition rate of 89.6% on a dataset with 5,200 frame images of $300$ vehicles recorded at Federal University of Minas Gerais (UFMG). In addition, this work also proposes a novel benchmark, designed specifically to evaluate character segmentation techniques, composed of a dataset of 2,000 Brazilian license plates (resulting in 14,000 alphanumeric symbols) and an evaluation protocol considering a novel evaluation measure, the Jaccard-Centroid coefficient. |
Artur Jordao The Good, the Fast and the Better Pedestrian Detector Masters Thesis Federal University of Minas Gerais, 2016. Resumo | Links | BibTeX | Tags: DeepEyes, DET, GigaFrames, Pedestrian Detection, VER+ @mastersthesis{Jordao:2016:MSc, title = {The Good, the Fast and the Better Pedestrian Detector}, author = {Artur Jordao}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_ArturJordao.pdf}, year = {2016}, date = {2016-06-24}, school = {Federal University of Minas Gerais}, abstract = {Pedestrian detection is a well-known problem in Computer Vision, mostly because of its direct applications in surveillance, transit safety and robotics. In the past decade, several efforts have been performed to improve the detection in terms of accuracy, speed and feature enhancement. In this work, we propose and analyze techniques focusing on these points. First, we develop an accurate oblique random forest (oRF) associated with Partial Least Squares (PLS). The method utilizes the PLS to find a decision surface, at each node of a decision tree, that better splits the samples presented to it, based on some purity criterion. To measure the advantages provided by PLS on the oRF, we compare the proposed method with the oRF based on SVM. Second, we evaluate and compare filtering approaches to reduce the search space and keep only potential regions of interest to be presented to detectors, speeding up the detection process. Experimental results demonstrate that the evaluated filters are able to discard a large number of detection windows without compromising the accuracy. Finally, we propose a novel approach to extract powerful features regarding the scene. The method combines results of distinct pedestrian detectors by reinforcing the human hypothesis, whereas suppressing a significant number of false positives due to the lack of spatial consensus when multiple detectors are considered. Our proposed approach, referred to as Spatial Consensus (SC), outperforms all previously published state-of-the-art pedestrian detection methods.}, keywords = {DeepEyes, DET, GigaFrames, Pedestrian Detection, VER+}, pubstate = {published}, tppubtype = {mastersthesis} } Pedestrian detection is a well-known problem in Computer Vision, mostly because of its direct applications in surveillance, transit safety and robotics. In the past decade, several efforts have been performed to improve the detection in terms of accuracy, speed and feature enhancement. In this work, we propose and analyze techniques focusing on these points. First, we develop an accurate oblique random forest (oRF) associated with Partial Least Squares (PLS). The method utilizes the PLS to find a decision surface, at each node of a decision tree, that better splits the samples presented to it, based on some purity criterion. To measure the advantages provided by PLS on the oRF, we compare the proposed method with the oRF based on SVM. Second, we evaluate and compare filtering approaches to reduce the search space and keep only potential regions of interest to be presented to detectors, speeding up the detection process. Experimental results demonstrate that the evaluated filters are able to discard a large number of detection windows without compromising the accuracy. Finally, we propose a novel approach to extract powerful features regarding the scene. The method combines results of distinct pedestrian detectors by reinforcing the human hypothesis, whereas suppressing a significant number of false positives due to the lack of spatial consensus when multiple detectors are considered. Our proposed approach, referred to as Spatial Consensus (SC), outperforms all previously published state-of-the-art pedestrian detection methods. |
Marco Tulio Alves Rodrigues Detecção de Mudanças em Cenas Terrestres usando Imagens Aéreas Tese PhD 2016. Resumo | Links | BibTeX | Tags: Change Detection @phdthesis{Rodrigues:2016:PhD, title = {Detecção de Mudanças em Cenas Terrestres usando Imagens Aéreas}, author = {Marco Tulio Alves Rodrigues}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_MarcoTulio.pdf}, year = {2016}, date = {2016-03-22}, abstract = {This study addresses the problem of change detection in landscapes using aerial images acquired at different times, important for many applications. The monitoring pipeline, for instance, the usual way to change detection is a task performed by human operators which evaluates a video of a monitoring camera and searches for changes in the scene from the comparison of image pairs. This procedure is prone to errors because it is a tedious task, therefore is a justification to automated method. Besides enabling the reduction of errors and speed up the monitoring process, a system automatic can be used as a filter to provide a set of key frames that should receive more attention from the operator. Thus, the system can help the operators on the decision making process regarding the actions to be performed. The basic procedure for detecting changes is to find a set of pixels or regions that are different in another test image. However, images acquired at different dates may be influenced by radiometric and registration factors. In other words, the influence of camera movement, lighting variation, and atmospheric variation must be minimized. One of the methods proposed extracts local descriptors in the image blocks and provides an estimate of change using a non-parametric model (KDE). Unlike background subtraction and remote sensing methods which are based on pixels and assume independence between them, the proposed approach not requires a complex learning phase and it is capable of detecting changes using only two images. The second method applies an image segmentation before make the matching of the similar regions. In the experiments, the proposed approaches are compared to techniques used in change detection. According to the results, the proposed approach based on non-parametric model outperforms other methods found in the literature, mainly due to the fact that the approach is more robust to lighting variation. The results also demonstrate that the approach is able to filter images that should be further analyzed by operators.}, keywords = {Change Detection}, pubstate = {published}, tppubtype = {phdthesis} } This study addresses the problem of change detection in landscapes using aerial images acquired at different times, important for many applications. The monitoring pipeline, for instance, the usual way to change detection is a task performed by human operators which evaluates a video of a monitoring camera and searches for changes in the scene from the comparison of image pairs. This procedure is prone to errors because it is a tedious task, therefore is a justification to automated method. Besides enabling the reduction of errors and speed up the monitoring process, a system automatic can be used as a filter to provide a set of key frames that should receive more attention from the operator. Thus, the system can help the operators on the decision making process regarding the actions to be performed. The basic procedure for detecting changes is to find a set of pixels or regions that are different in another test image. However, images acquired at different dates may be influenced by radiometric and registration factors. In other words, the influence of camera movement, lighting variation, and atmospheric variation must be minimized. One of the methods proposed extracts local descriptors in the image blocks and provides an estimate of change using a non-parametric model (KDE). Unlike background subtraction and remote sensing methods which are based on pixels and assume independence between them, the proposed approach not requires a complex learning phase and it is capable of detecting changes using only two images. The second method applies an image segmentation before make the matching of the similar regions. In the experiments, the proposed approaches are compared to techniques used in change detection. According to the results, the proposed approach based on non-parametric model outperforms other methods found in the literature, mainly due to the fact that the approach is more robust to lighting variation. The results also demonstrate that the approach is able to filter images that should be further analyzed by operators. |
Cassio Elias Santos dos Junior; Ewa Kijak; Guillaume Gravier; William Robson Schwartz Partial least squares for face hashing Journal Article Neurocomputing, 213 , pp. 34–47, 2016. Links | BibTeX | Tags: Face Identification, Face Recognition, Featured Publication, Partial Least Squares @article{Santos:2016:Neurocomputing, title = {Partial least squares for face hashing}, author = {Cassio Elias Santos dos Junior and Ewa Kijak and Guillaume Gravier and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_Neurocomputing_Santos.pdf}, year = {2016}, date = {2016-02-03}, journal = {Neurocomputing}, volume = {213}, pages = {34--47}, keywords = {Face Identification, Face Recognition, Featured Publication, Partial Least Squares}, pubstate = {published}, tppubtype = {article} } |
Antonio Carlos Nazare Junior; William Robson Schwartz A scalable and flexible framework for smart video surveillance Journal Article Computer Vision and Image Understanding, 144 (C), pp. 258–275, 2016. Links | BibTeX | Tags: Smart Surveillance, Smart Surveillance Framework, SSF, Surveillance Systems, VER+, Video Surveillance @article{Nazare:2016:CVIU, title = {A scalable and flexible framework for smart video surveillance}, author = {Antonio Carlos Nazare Junior and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_CVIU.pdf}, year = {2016}, date = {2016-01-01}, journal = {Computer Vision and Image Understanding}, volume = {144}, number = {C}, pages = {258--275}, keywords = {Smart Surveillance, Smart Surveillance Framework, SSF, Surveillance Systems, VER+, Video Surveillance}, pubstate = {published}, tppubtype = {article} } |
Marco Tulio Alves Rodrigues; Daniel Balbino de Mesquita; Erickson R Nascimento; William Robson Schwartz Change detection based on feature invariant to monotonic transforms and spatially constrained matching Journal Article Journal of Electronic Imaging, 25 (1), pp. 1-10, 2016. Links | BibTeX | Tags: Change Detection @article{Rodrigues:2016:JEI, title = {Change detection based on feature invariant to monotonic transforms and spatially constrained matching}, author = {Marco Tulio Alves Rodrigues and Daniel Balbino de Mesquita and Erickson R Nascimento and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_JEI.pdf}, year = {2016}, date = {2016-01-01}, journal = {Journal of Electronic Imaging}, volume = {25}, number = {1}, pages = {1-10}, keywords = {Change Detection}, pubstate = {published}, tppubtype = {article} } |