Spatial Transformer Network on Skeleton-based Gait Recognition

• URL: http://arxiv.org/abs/2204.03873v1
• Date: Fri, 8 Apr 2022 06:53:23 GMT
• Title: Spatial Transformer Network on Skeleton-based Gait Recognition
• Authors: Cun Zhang, Xing-Peng Chen, Guo-Qiang Han, Xiang-Jie Liu
• Abstract summary: Gait-TR is a robust skeleton-based gait recognition model based on spatial transformer frameworks and temporal convolutional networks. Gait-TR achieves substantial improvements over other skeleton-based gait models with higher accuracy and better robustness on the well-known gait dataset CASIA-B.
• Score: 19.747638780327257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skeleton-based gait recognition models usually suffer from the robustness problem, as the Rank-1 accuracy varies from 90\% in normal walking cases to 70\% in walking with coats cases. In this work, we propose a state-of-the-art robust skeleton-based gait recognition model called Gait-TR, which is based on the combination of spatial transformer frameworks and temporal convolutional networks. Gait-TR achieves substantial improvements over other skeleton-based gait models with higher accuracy and better robustness on the well-known gait dataset CASIA-B. Particularly in walking with coats cases, Gait-TR get a 90\% Rank-1 gait recognition accuracy rate, which is higher than the best result of silhouette-based models, which usually have higher accuracy than the silhouette-based gait recognition models. Moreover, our experiment on CASIA-B shows that the spatial transformer can extract gait features from the human skeleton better than the widely used graph convolutional network.

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