@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}
}