LTA-LCNet: enhancing PP-LCNetv2 with local temporal attention for tennis swing analysis using TSAD

An Zhu; LinQian Fu; Zhen Yang; Zhijian Yin; Chen Yao

doi:10.1117/12.3049593

8 November 2024 LTA-LCNet: enhancing PP-LCNetv2 with local temporal attention for tennis swing analysis using TSAD

An Zhu, LinQian Fu, Zhen Yang, Zhijian Yin, Chen Yao

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134163I (2024) https://doi.org/10.1117/12.3049593
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

The aim of this study is to develop a practical tennis swing recognition system that helps beginners to learn the correct swing technique, correct incorrect movements and provide functions for counting and evaluating swings. To achieve this goal, we created a dataset called TSAD that is specifically tailored to tennis swing actions, like the public UCF101 dataset, to support model training and evaluation. We used an extended PP-TSMv2 model with local temporal attention (LTA) replacing the original global attention mechanism in the training dataset. The model was trained and evaluated on both the public UCF101 dataset and TSAD and showed significantly improved performance over the original model. The results of this study indicate that the tennis swing recognition system based on the improved PP-TSMv2 model has potential practical value, providing effective training and guidance for tennis players and forming the basis for further research and applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

An Zhu, LinQian Fu, Zhen Yang, Zhijian Yin, and Chen Yao "LTA-LCNet: enhancing PP-LCNetv2 with local temporal attention for tennis swing analysis using TSAD", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134163I (8 November 2024); https://doi.org/10.1117/12.3049593

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KEYWORDS

Data modeling

Education and training

Video

Performance modeling

Action recognition

Motion models

Analytical research

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