Tennis Action Evaluation Model Based on Weighted Counter Clockwise Rotation Angle Similarity Measurement Method

In order to intelligently analyze tennis movements and improve evaluation efficiency, a counter clockwise rotation angle of limbs is proposed to solve the direction problem of tennis movements. A dynamic time regularization algorithm is optimized by combining global time weighting and adjacent frame weighting. The results indicated that the proposed counter clockwise rotation angle feature of limbs could effectively represent changes in limb direction and clearly distinguish action postures. The average accuracy of this method in action classification on the Tennis Stroke Dataset was 97.60%. In the action evaluation mode, the average frame rate of the client was between 17.35FPS and 17.49FPS, and the overall average frame rate was about 17.40FPS. The server exhibits higher efficiency in action processing and evaluation, which can process video frames faster. It is more efficient in processing data capabilities and utilizing data resources. This indicates that the performance of the system is relatively consistent in different modes and has stability. The optimized method has a higher generalization ability in recognizing non-tennis movements on different datasets. When dealing with fine movements, the optimized method performs excellently and can better capture subtle differences in the movements. Meanwhile, this enhances the real-time performance of the system, making it suitable for evaluating tennis movements in practical application scenarios. This provides a new technical path for analyzing tennis movements and also serves as a reference for evaluating movements in other sports.

@article{Jiang2024,
  title     = {Tennis Action Evaluation Model Based on Weighted Counter Clockwise Rotation Angle Similarity Measurement Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Danni Jiang and Ge Liu},
  doi       = {10.14569/IJACSA.2024.0151119},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151119}
}