An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions
Weijia Li, Saihui Hou, Chunjie Zhang, Chunshui Cao, Xu Liu, Yongzhen Huang, Yao Zhao
Abstract
The target of person re-identification (ReID) and gait recognition is consistent, that is to match the target pedestrian under surveillance cameras. For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions. To tackle this problem, we propose a Cloth-Changing benchmark for Person re-identification and Gait recognition (CCPG). It is a cloth-changing dataset, and there are several highlights in CCPG, (1) it provides 200 identities and over 16K sequences are captured indoors and outdoors, (2) each identity has seven different cloth-changing statuses, which is hardly seen in previous datasets, (3) RGB and silhouettes version data are both available for research purposes. Moreover, aiming to investigate the cloth-changing problem systematically, comprehensive experiments are conducted on video-based ReID and gait recognition methods. The experimental results demonstrate the superiority of ReID and gait recognition separately in different cloth-changing conditions and suggest that gait recognition is a potential solution for addressing the cloth-changing problem. Our dataset will be available at https://github.com/BNU-IVC/CCPG.
BibTeX
@inproceedings{cvpr2023_anindepthexplora,
title = {An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions},
author = {Weijia Li and Saihui Hou and Chunjie Zhang and Chunshui Cao and Xu Liu and Yongzhen Huang and Yao Zhao},
booktitle = {CVPR 2023},
year = {2023}
}