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Frontiers in Sport Research, 2022, 4(5); doi: 10.25236/FSR.2022.040507.

Multi-Target Detection of Table Tennis Video Based on CNN and Yolov3


Wanyue Li, Jie Cui

Corresponding Author:
Wanyue Li

School of Economics and Management, Shanghai University of Sports, Shanghai, China


In order to explore the effect of convolutional neural network (CNN) on the detection of athletes and balls in table tennis, and to solve the problems of low accuracy and weak generalization ability of table tennis athletes training data, this paper analyzed videos of table tennis athletes. For the detection of multiple targets in the videos, including athletes and balls, we used Yolov3 as a deep learning framework, and CNN as an automatic detection method when processing images. We trained and test the video data to improve the stability and accuracy of target detection, through modifying its model on the basis of the Yolov3 model. Finally, we detect the movement trajectories of athletes and balls in table tennis videos stably, and the accuracy is above 0.8.


Convolutional Neural Network; Yolov3 Model; Table Tennis; Multi-Target Detection

Cite This Paper

Wanyue Li, Jie Cui. Multi-Target Detection of Table Tennis Video Based on CNN and Yolov3. Frontiers in Sport Research (2022) Vol. 4, Issue 5: 24-28. https://doi.org/10.25236/FSR.2022.040507.


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