Spatio-Temporal Tube Self-Attention Mechanism for Quality Assessment of Skiing Actions
Currently, there is an increasing demand for scientific sports training methods. Existing action analysis approaches suffer from high computational complexity, inefficient feature aggregation, and strong background interference. To address these issues, this paper proposes a spatio-temporal tube self-attention action quality assessment method for skiing sport scenarios. Firstly, a real skiing dataset containing multiple postures and scenes is constructed, annotated with Human3.6M skeleton format keypoints and expert scores. Secondly, a lightweight OpenPose improvement by using CSP-Darknet53 as the backbone is proposed, reducing computational complexity while maintaining detection accuracy. Thirdly, a Tube Self-Attention Network (TSA-Net) is designed, which generates spatio-temporal tubes via SiamMask object tracking, performs sparse self-attention feature aggregation inside the tube, and fuses pose features for quality scoring of multimodal actions. Finally, Experimental results shown that TSA-Net significantly outperforms baseline networks on skiing actions classification tasks, validating its effectiveness in optimizing computational efficiency, enhancing feature discriminability, and suppressing background interference.