Spatial Transformer Network on Skeleton-based Gait Recognition
This improves gait recognition robustness for applications like surveillance, though it is incremental as it builds on existing spatial transformer and temporal convolutional methods.
The paper tackled the robustness problem in skeleton-based gait recognition, where accuracy drops from 90% in normal walking to 70% with coats, by proposing Gait-TR, which achieved 90% Rank-1 accuracy in walking with coats cases on the CASIA-B dataset.
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.