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} } |
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} } |
2016 |
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. |
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} } |
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} } |
2016 |
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. |