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Movement Detection of Tennis Players Based on Yolo and Human Skeleton Recognition Technology
- 1 Maynooth International Engineering College, Fuzhou University
- 2 School of Beijing Foreign Study, University International Curriculum Center
- 3 School of Beijing Foreign Study, University International Curriculum Center
* Author to whom correspondence should be addressed.
Abstract
This study aims to develop a monitoring model for the action states of athletes (such as standing, moving, and striking) during tennis matches. The model is based on the YOLO (You Only Look Once) architecture and is trained with the 3dResNet50 to achieve automatic recognition of athletes’ actions. Additionally, we utilized the MediaPipe model to recognize the human skeletal structure, further enhancing the accuracy of action recognition. Tested on real tennis match video data, the model demonstrated efficient action recognition capabilities and good real-time performance, providing strong technical support for sports training and competition analysis. This research not only extends the application of computer vision in the field of sports but also lays a foundation for further advancements in motion analysis technology.
Keywords
Behavior Recognition, YOLO, 3DResNet50, Media-pipe, STGCN model, Frame Compare
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Cite this article
He,M.;Fu,M.;Cao,D. (2025). Movement Detection of Tennis Players Based on Yolo and Human Skeleton Recognition Technology. Applied and Computational Engineering,108,100-107.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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