Spatio-temporal dual-attention network for view-invariant human action recognition

Abstract

Due to the action occlusion and information loss caused by the view changes, view-invariant human action recognition is challenging in plenty of real-world applications. One possible solution to this problem is minimizing representation discrepancy in different views while learning discriminative feature representation for view-invariant action recognition. To solve the problem, we propose a Spatio-temporal Dual-Attention Network (SDA-Net) for view-invariant human action recognition. The SDA-Net is composed of a spatial/temporal self-attention and spatial/temporal cross-attention modules. The spatial/temporal self-attention module captures global long-range dependencies of action features. The cross-attention module is designed to learn view-invariant co-occurrence attention maps and generates discriminative features for a semantic representation of actions in different views. We exhaustively evaluate our approach on the NTU- 60, NTU-120, and UESTC datasets with multi-type evaluations, i.e., Cross-Subject, Cross-View, Cross-Set, and Arbitrary-view. Extensive experiment results demonstrate that our approach exceeds the state-of-the-art approaches with a significant margin in view-invariant human action recognition.