Spatial-Temporal Graph Attention Network for Video-Based Gait Recognition

Wu, Xinhui1; An, Weizhi1; Yu, Shiqi2,3; Guo, Weiyu4; García, Edel B.5

10.1007/978-3-030-41299-9_22

2020

16113349

5th Asian Conference on Pattern Recognition, ACPR 2019

12047 LNCS

274-286

Auckland, New zealand

Springer

Gait is a kind of attractive feature for human identification at a distance. It can be regarded as a kind of temporal signal. At the same time the human body shape can be regarded as the signal in the spatial domain. In the proposed method, we try to extract discriminative feature from video sequences in the spatial and temporal domains by only one network, Spatial-Temporal Graph Attention Network (STGAN). In spatial domain, we designed one branch to select some distinguished regions and enhance their contribution. It can make the network focus on these distinguished regions. We also constructed another branch, a Spatial-Temporal Graph (STG), to discover the relationship between frames and the variation of a region in temporal domain. The proposed method can extract gait feature in the two domains, and the two branches in the model can be trained end to end. The experimental results on two popular datasets, CASIA-B and OU-ISIR Treadmill-B, show the proposed method can improve gait recognition obviously.
© 2020, Springer Nature Switzerland AG.

通讯

EI

20201208308321

Gait analysis

Biomechanics, Bionics and Biomimetics:461.3

EV Compendex

1.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
2.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
3.Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
4.Watrix Technology Limited Co., Ltd., Beijing, China
5.Advanced Technologies Application Center (CENATAV), Havana, Cuba

Wu, Xinhui,An, Weizhi,Yu, Shiqi,et al. Spatial-Temporal Graph Attention Network for Video-Based Gait Recognition[C]:Springer,2020:274-286.