Person Re-Identification With Correspondence Structure Learning
Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang
Abstract
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Experimental results on various datasets demonstrate the effectiveness of our approach.
BibTeX
@inproceedings{iccv2015_personreidentifi,
title = {Person Re-Identification With Correspondence Structure Learning},
author = {Yang Shen and Weiyao Lin and Junchi Yan and Mingliang Xu and Jianxin Wu and Jingdong Wang},
booktitle = {ICCV 2015},
year = {2015}
}