Abstract: Badminton, as a popular sport in the field of sports, has rich information on body motions and motion trajectories. Accurately identifying the swinging motions during badminton is of great significance for badminton education, promotion, and competition. Therefore, based on the framework of Faster R-CNN multi object tracking algorithm, a new badminton tracking and motion evaluation model is proposed by introducing a VGG19 network architecture and real-time multi person pose estimation algorithm for performance optimization. The experimental results showed that the new badminton tracking and motion evaluation model achieved an average processing speed of 31.02 frames per second for five bone points in the human head, shoulder, elbow, wrist, and neck. Its accuracy in detecting the highest percentage of correct key points for the head, shoulders, elbows, wrists, and neck reached 98.05%, 98.10%, 97.89%, 97.55%, and 98.26%, respectively. The minimum values of mean square error and mean absolute error were only 0.021 and 0.026. The highest resource consumption rate was only 6.85%, and the highest accuracy of motion evaluation was 97.71%. In addition, indoor and outdoor environments had almost no impact on the performance of the model. In summary, the study aims to improve the fast region convolutional neural network and apply it to badminton tracking and motion evaluation with higher effectiveness and recognition accuracy. This study aims to demonstrate a more effective approach for the development of badminton sports.

Jun Ou, Chao Fu and Yanyun Cao, “Badminton Tracking and Motion Evaluation Model Based on Faster RCNN and Improved VGG19” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151017

@article{Ou2024,
title = {Badminton Tracking and Motion Evaluation Model Based on Faster RCNN and Improved VGG19},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151017},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151017},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Jun Ou and Chao Fu and Yanyun Cao}
}