Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry

The intelligent tracking and detection of athletes' actions and the improvement of action standardization are of great practical significance to reducing the injury caused by sports in the sports industry. For the problems of nonstandard movement and single movement mode, this exploration takes the video of sports events as the object and combines it with the video general feature extraction of convolutional neural network (CNN) in the field of deep learning and the filtering detection algorithm of motion trajectory. Then, a target detection and tracking system model is proposed to track and detect targets in sports in real-time. Moreover, through experiments, the performance of the proposed system model is analyzed. After testing the detection quantity, response rate, data loss rate, and target detection accuracy of the model, the results show that the model can track and monitor 50 targets with a loss rate of 3%, a response speed of 4 s and a target detection accuracy of 80%. It can play an excellent role in sports events and postgame video analysis, and provide a good basis and certain design ideas for the goal tracking of the sports industry.