Player Performance Analysis in Table Tennis Through Human Action Recognition

This paper aims to enhance the effectiveness of table tennis coaching and player performance analysis through human action recognition by using deep learning. In the field of video analysis, human action recognition has emerged as a highly researched area. Beyond post-session analysis, it has the potential for real-time applications, such as providing instant feedback or comparing ideal motions with actual player movements. However, the complexity of human actions presents significant challenges. To address these issues, in this paper, we combine the latest computer vision and deep learning algorithms to accurately identify and classify a few table tennis strokes in human action recognition. Through an in-depth review of existing methods, we develop a high-precision offline method for player action recognition. Our experimental results show that the proposed method achieves an average accuracy of 99.85% in recognizing six distinct table tennis actions based on our own dataset.