@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}
}

@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}
}

@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}
}

@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}
}