2018 |
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} } |
Kloss, Ricardo Barbosa Boosted Projections and Low Cost Transfer Learning Applied to Smart Surveillance Masters Thesis Federal University of Minas Gerais, 2018. Resumo | Links | BibTeX | Tags: Computer vision, Deep Learning, Forensics, Machine Learning, Surveillance @mastersthesis{Kloss2018, title = {Boosted Projections and Low Cost Transfer Learning Applied to Smart Surveillance}, author = {Ricardo Barbosa Kloss}, url = {https://www.dropbox.com/s/dpkkv16zkardxdx/main.pdf?dl=0}, year = {2018}, date = {2018-02-01}, school = {Federal University of Minas Gerais}, abstract = {Computer vision is an important area related to understanding the world through images. It can be used as biometry, by verifying if a given face is of a certain identity, used to look for crime perpetrators in an airport blacklist, used in human-machine interactions and other goals. Since 2012, deep learning methods have become ubiquitous in computer vision achieving breakthroughs, and making possible for machines, for instance, to perform face verification with human-level skill. This work tackles two computer vision problems and is divided in two parts. In one we explore deep learning methods in the task of face verification and in the other the task of dimensionality reduction. Both tasks have large importance in the fields of machine learning and computer vision. We focus on their application in smart surveillance. Dimensionality reduction helps alleviate problems which usually suffer from a very high dimensionality, which can make it hard to learn classifiers. This work presents a novel method for tackling this problem, referred to as Boosted Projection. It relies on the use of several projection models based on Principal Component Analysis or Partial Least Squares to build a more compact and richer data representation. Our experimental results demonstrate that the proposed approach outperforms many baselines and provides better results when compared to the original dimensionality reduction techniques of partial least squares. In the second part of this work, regarding face verification, we explored a simple and cheap technique to extract deep features and reuse a pre-learned model. The technique is a transfer learning that involves no fine-tuning of the model to the new domain. Namely, we explore the correlation of depth and scale in deep models, and look for the layer/scale that yields the best results for the new domain, we also explore metrics for the verification task, using locally connected convolutions to learn distance metrics. Our face verification experiments use a model pre-trained in face identification and adapt it to the face verification task with different data, but still on the face domain. We achieve 96.65% mean accuracy on the Labeled Faces in the Wild dataset and 93.12% mean accuracy on the Youtube Faces dataset which are in the state-of-the-art.}, keywords = {Computer vision, Deep Learning, Forensics, Machine Learning, Surveillance}, pubstate = {published}, tppubtype = {mastersthesis} } Computer vision is an important area related to understanding the world through images. It can be used as biometry, by verifying if a given face is of a certain identity, used to look for crime perpetrators in an airport blacklist, used in human-machine interactions and other goals. Since 2012, deep learning methods have become ubiquitous in computer vision achieving breakthroughs, and making possible for machines, for instance, to perform face verification with human-level skill. This work tackles two computer vision problems and is divided in two parts. In one we explore deep learning methods in the task of face verification and in the other the task of dimensionality reduction. Both tasks have large importance in the fields of machine learning and computer vision. We focus on their application in smart surveillance. Dimensionality reduction helps alleviate problems which usually suffer from a very high dimensionality, which can make it hard to learn classifiers. This work presents a novel method for tackling this problem, referred to as Boosted Projection. It relies on the use of several projection models based on Principal Component Analysis or Partial Least Squares to build a more compact and richer data representation. Our experimental results demonstrate that the proposed approach outperforms many baselines and provides better results when compared to the original dimensionality reduction techniques of partial least squares. In the second part of this work, regarding face verification, we explored a simple and cheap technique to extract deep features and reuse a pre-learned model. The technique is a transfer learning that involves no fine-tuning of the model to the new domain. Namely, we explore the correlation of depth and scale in deep models, and look for the layer/scale that yields the best results for the new domain, we also explore metrics for the verification task, using locally connected convolutions to learn distance metrics. Our face verification experiments use a model pre-trained in face identification and adapt it to the face verification task with different data, but still on the face domain. We achieve 96.65% mean accuracy on the Labeled Faces in the Wild dataset and 93.12% mean accuracy on the Youtube Faces dataset which are in the state-of-the-art. |
2017 |
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} } |
2015 |
Peixoto, Sirlene; Gonçalves, Gabriel Resende; Camara-Chavez, Guillermo; Schwartz, William Robson; Gomes, David Menotti Brazilian License Plate Character Recognition using Deep Learning Inproceedings Workshop em Visao Computacional (WVC), pp. 1-5, 2015. Links | BibTeX | Tags: Automatic License Plate Recognition, Deep Learning @inproceedings{Peixoto:2015:WVC, title = {Brazilian License Plate Character Recognition using Deep Learning}, author = {Sirlene Peixoto and Gabriel Resende Gonçalves and Guillermo Camara-Chavez and William Robson Schwartz and David Menotti Gomes}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_WVC_Peixoto.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Workshop em Visao Computacional (WVC)}, pages = {1-5}, keywords = {Automatic License Plate Recognition, Deep Learning}, pubstate = {published}, tppubtype = {inproceedings} } |
Menotti, D; Chiachia, G; Pinto, A; Schwartz, William Robson; Pedrini, H; Falcao, Xavier A; Rocha, A Deep Representations for Iris, Face, and Fingerprint Spoofing Detection Journal Article Information Forensics and Security, IEEE Transactions on, 10 (4), pp. 864-879, 2015, ISSN: 1556-6013. Links | BibTeX | Tags: Deep Learning, DeepEyes, DET, SmartView, Spoofing Detection @article{Menotti:2015:TIFS, title = {Deep Representations for Iris, Face, and Fingerprint Spoofing Detection}, author = {D Menotti and G Chiachia and A Pinto and William Robson Schwartz and H Pedrini and Xavier A Falcao and A Rocha}, url = {http://dx.doi.org/10.1109/TIFS.2015.2398817}, issn = {1556-6013}, year = {2015}, date = {2015-01-01}, journal = {Information Forensics and Security, IEEE Transactions on}, volume = {10}, number = {4}, pages = {864-879}, keywords = {Deep Learning, DeepEyes, DET, SmartView, Spoofing Detection}, pubstate = {published}, tppubtype = {article} } |
2018 |
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} } |
Ricardo Barbosa Kloss Boosted Projections and Low Cost Transfer Learning Applied to Smart Surveillance Masters Thesis Federal University of Minas Gerais, 2018. Resumo | Links | BibTeX | Tags: Computer vision, Deep Learning, Forensics, Machine Learning, Surveillance @mastersthesis{Kloss2018, title = {Boosted Projections and Low Cost Transfer Learning Applied to Smart Surveillance}, author = {Ricardo Barbosa Kloss}, url = {https://www.dropbox.com/s/dpkkv16zkardxdx/main.pdf?dl=0}, year = {2018}, date = {2018-02-01}, school = {Federal University of Minas Gerais}, abstract = {Computer vision is an important area related to understanding the world through images. It can be used as biometry, by verifying if a given face is of a certain identity, used to look for crime perpetrators in an airport blacklist, used in human-machine interactions and other goals. Since 2012, deep learning methods have become ubiquitous in computer vision achieving breakthroughs, and making possible for machines, for instance, to perform face verification with human-level skill. This work tackles two computer vision problems and is divided in two parts. In one we explore deep learning methods in the task of face verification and in the other the task of dimensionality reduction. Both tasks have large importance in the fields of machine learning and computer vision. We focus on their application in smart surveillance. Dimensionality reduction helps alleviate problems which usually suffer from a very high dimensionality, which can make it hard to learn classifiers. This work presents a novel method for tackling this problem, referred to as Boosted Projection. It relies on the use of several projection models based on Principal Component Analysis or Partial Least Squares to build a more compact and richer data representation. Our experimental results demonstrate that the proposed approach outperforms many baselines and provides better results when compared to the original dimensionality reduction techniques of partial least squares. In the second part of this work, regarding face verification, we explored a simple and cheap technique to extract deep features and reuse a pre-learned model. The technique is a transfer learning that involves no fine-tuning of the model to the new domain. Namely, we explore the correlation of depth and scale in deep models, and look for the layer/scale that yields the best results for the new domain, we also explore metrics for the verification task, using locally connected convolutions to learn distance metrics. Our face verification experiments use a model pre-trained in face identification and adapt it to the face verification task with different data, but still on the face domain. We achieve 96.65% mean accuracy on the Labeled Faces in the Wild dataset and 93.12% mean accuracy on the Youtube Faces dataset which are in the state-of-the-art.}, keywords = {Computer vision, Deep Learning, Forensics, Machine Learning, Surveillance}, pubstate = {published}, tppubtype = {mastersthesis} } Computer vision is an important area related to understanding the world through images. It can be used as biometry, by verifying if a given face is of a certain identity, used to look for crime perpetrators in an airport blacklist, used in human-machine interactions and other goals. Since 2012, deep learning methods have become ubiquitous in computer vision achieving breakthroughs, and making possible for machines, for instance, to perform face verification with human-level skill. This work tackles two computer vision problems and is divided in two parts. In one we explore deep learning methods in the task of face verification and in the other the task of dimensionality reduction. Both tasks have large importance in the fields of machine learning and computer vision. We focus on their application in smart surveillance. Dimensionality reduction helps alleviate problems which usually suffer from a very high dimensionality, which can make it hard to learn classifiers. This work presents a novel method for tackling this problem, referred to as Boosted Projection. It relies on the use of several projection models based on Principal Component Analysis or Partial Least Squares to build a more compact and richer data representation. Our experimental results demonstrate that the proposed approach outperforms many baselines and provides better results when compared to the original dimensionality reduction techniques of partial least squares. In the second part of this work, regarding face verification, we explored a simple and cheap technique to extract deep features and reuse a pre-learned model. The technique is a transfer learning that involves no fine-tuning of the model to the new domain. Namely, we explore the correlation of depth and scale in deep models, and look for the layer/scale that yields the best results for the new domain, we also explore metrics for the verification task, using locally connected convolutions to learn distance metrics. Our face verification experiments use a model pre-trained in face identification and adapt it to the face verification task with different data, but still on the face domain. We achieve 96.65% mean accuracy on the Labeled Faces in the Wild dataset and 93.12% mean accuracy on the Youtube Faces dataset which are in the state-of-the-art. |
2017 |
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} } |
2015 |
Sirlene Peixoto; Gabriel Resende Gonçalves; Guillermo Camara-Chavez; William Robson Schwartz; David Menotti Gomes Brazilian License Plate Character Recognition using Deep Learning Inproceedings Workshop em Visao Computacional (WVC), pp. 1-5, 2015. Links | BibTeX | Tags: Automatic License Plate Recognition, Deep Learning @inproceedings{Peixoto:2015:WVC, title = {Brazilian License Plate Character Recognition using Deep Learning}, author = {Sirlene Peixoto and Gabriel Resende Gonçalves and Guillermo Camara-Chavez and William Robson Schwartz and David Menotti Gomes}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_WVC_Peixoto.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Workshop em Visao Computacional (WVC)}, pages = {1-5}, keywords = {Automatic License Plate Recognition, Deep Learning}, pubstate = {published}, tppubtype = {inproceedings} } |
D Menotti; G Chiachia; A Pinto; William Robson Schwartz; H Pedrini; Xavier A Falcao; A Rocha Deep Representations for Iris, Face, and Fingerprint Spoofing Detection Journal Article Information Forensics and Security, IEEE Transactions on, 10 (4), pp. 864-879, 2015, ISSN: 1556-6013. Links | BibTeX | Tags: Deep Learning, DeepEyes, DET, SmartView, Spoofing Detection @article{Menotti:2015:TIFS, title = {Deep Representations for Iris, Face, and Fingerprint Spoofing Detection}, author = {D Menotti and G Chiachia and A Pinto and William Robson Schwartz and H Pedrini and Xavier A Falcao and A Rocha}, url = {http://dx.doi.org/10.1109/TIFS.2015.2398817}, issn = {1556-6013}, year = {2015}, date = {2015-01-01}, journal = {Information Forensics and Security, IEEE Transactions on}, volume = {10}, number = {4}, pages = {864-879}, keywords = {Deep Learning, DeepEyes, DET, SmartView, Spoofing Detection}, pubstate = {published}, tppubtype = {article} } |