Paper Info
Title
Spatial-Temporal Graph Convolutional Network For Video-Based Person Re-Identification
Abstract
While video-based person re-identification (Re-ID) has drawn increasing attention and made great progress in recent years, it is still very challenging to effectively overcome the occlusion problem and the visual ambiguity problem for visually similar negative samples. On the other hand, we observe that different frames of a video can provide complementary information for each other, and the structural information of pedestrians can provide extra discriminative cues for appearance features. Thus, modeling the temporal relations of different frames and the spatial relations within a frame has the potential for solving the above problems. In this work, we propose a novel Spatial-Temporal Graph Convolutional Network (STGCN) to solve these problems. The STGCN includes two GCN branches, a spatial one and a temporal one. The spatial branch extracts structural information of a human body. The temporal branch mines discriminative cues from adjacent frames. By jointly optimizing these branches, our model extracts robust spatialtemporal information that is complementary with appearance information. As shown in the experiments, our model achieves state-of-the-art results on MARS and DukeMTMC-VideoReID datasets.
Year
DOI
Venue
2020
10.1109/CVPR42600.2020.00335
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
DocType
ISSN
Citations 
Conference
1063-6919
5
PageRank 
References 
Authors
0.38
40
5
Name
Order
Citations
PageRank
Jinrui Yang150.38
Wei-Shi Zheng22915140.63
Qize Yang3302.08
Ying-Cong Chen41658.38
Qi Tian56443331.75