Transformer-based multiview spatiotemporal feature interactive fusion for human action recognition in depth videos

•A transformer-based framework for the interactive fusion of multiview spatiotemporal features, facilitating effective action recognition through deep integration of multiview information.•Multiview depth dynamic volumes to capture 3D spatiotemporal evolution of human actions.•Intra-view spatiotemporal feature modeling to learn global context dependency within each view.•Cross-view feature interactive fusion using cross-attention for deep inter-view feature integration. Spatiotemporal feature modeling is the key to human action recognition task. Multiview data is helpful in acquiring numerous clues to improve the robustness and accuracy of feature description. However, multiview action recognition has not been well explored yet. Most existing methods perform action recognition only from a single view, which leads to the limited performance. Depth data is insensitive to illumination and color variations and offers significant advantages by providing reliable 3D geometric information of the human body. In this study, we concentrate on action recognition from depth videos and introduce a transformer-based framework for the interactive fusion of multiview spatiotemporal features, facilitating effective action recognition through deep integration of multiview information. Specifically, the proposed framework consists of intra-view spatiotemporal feature modeling (ISTFM) and cross-view feature interactive fusion (CFIF). Firstly, we project a depth video into three orthogonal views to construct multiview depth dynamic volumes that describe the 3D spatiotemporal evolution of human actions. ISTFM takes multiview depth dynamic volumes as input to extract spatiotemporal features of three views with 3D CNN, then applies self-attention mechanism in transformer to model global context dependency within each view. CFIF subsequently extends self-attention into cross-attention to conduct deep interaction between different views, and further integrates cross-view features together to generate a multiview joint feature representation. Our proposed method is tested on two large-scale RGBD datasets by extensive experiments to demonstrate the remarkable improvement for enhancing the recognition performance.