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

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.