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<p>To achieve accurate estimation and recognition of sports movements, an action pose estimation model was first constructed based on an improved graph convolutional network. Then, a motion recognition model was designed based on a dynamic spatiotemporal graph convolutional network. The experimental results showed that the motion pose estimation model designed in this study could detect a joint percentage of 0.986, and the success rate of joint point detection was 0.975. Compared with the real labels, the estimated joint points were the most similar, with a maximum average pose point position error of only 0.25. The action recognition model based on a dynamic spatiotemporal graph convolutional network achieved the best recognition accuracy and efficiency, with an average accuracy of 0.959, a frame rate of 192.988 FPS, and a computational load of only 98.754 FLOPs. Its Top-1 and Top-5 accuracies were as high as 0.96 and 0.97, respectively, indicating a strong ability to identify behavior from different perspectives and individuals. This study improves sports performance and advances sports research by accurately recognizing and estimating the movements of athletes.</p>

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Sports action estimation and recognition based on dynamic spatiotemporal graph convolution

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

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  • Fengling Zhang,
  • Huiyang Xiao,
  • Weitao Guo

摘要

To achieve accurate estimation and recognition of sports movements, an action pose estimation model was first constructed based on an improved graph convolutional network. Then, a motion recognition model was designed based on a dynamic spatiotemporal graph convolutional network. The experimental results showed that the motion pose estimation model designed in this study could detect a joint percentage of 0.986, and the success rate of joint point detection was 0.975. Compared with the real labels, the estimated joint points were the most similar, with a maximum average pose point position error of only 0.25. The action recognition model based on a dynamic spatiotemporal graph convolutional network achieved the best recognition accuracy and efficiency, with an average accuracy of 0.959, a frame rate of 192.988 FPS, and a computational load of only 98.754 FLOPs. Its Top-1 and Top-5 accuracies were as high as 0.96 and 0.97, respectively, indicating a strong ability to identify behavior from different perspectives and individuals. This study improves sports performance and advances sports research by accurately recognizing and estimating the movements of athletes.