Efficient Gait Recognition via Spatial-Temporal Decoupled Network

• Published in: Proceedings of ... International Joint Conference on Neural Networks pp. 1 - 8
• Main Authors: Tang, Peisen, Su, Han, Gao, Ruixuan, Zhao, Wensheng, Tang, Chaoying
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.06.2023
• Subjects: Computational modeling; Decoupled Parallel; Feature extraction; Gait Recognition; Interference; Solid modeling; Source coding; Spatial databases; Spatial-temporal Decoupling; Three-dimensional displays
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN54540.2023.10191622

Challenges in gait recognition arise from the difficulty of subject identification through subtle spatial differences. Learning the temporal representation helps, however, straightforward temporal modeling like 3D Convolution not only incurs excessive computational complexity, but may also further interfere with the already limited spatial details. In this paper, we present a Spatial-Temporal Decoupled Network (STDN) to balance the trade-off between the cross-frame interference, computational complexity, and model performance. Specifically, motivated by the concept of spatial-temporal decoupling, we propose a Decoupled Parallel Feature Extractor (DPFE) for noise-suppressed spatial-temporal features extraction. By incorporating DPFE as the basic component, we build a Efficient Spatial-temporal Awareness Block (ESA Block) that accounts for the unique properties of gait data, namely, restricted spatial information and abundant temporal information, to minimize the computational overhead imposed by temporal modeling. Experiments on CASIA-B and OU-MVLP datasets demonstrate the state-of-the-art performance of our proposed method, with an average rank-1 accuracy of 93.7% on the CASIA-B and 90.5% on the OU-MVLP. The source code is available on https://github.com/tk59854/stdn.