Deep Learning-Based Detection and Identification Method for Sports Health Video Dissemination

Abstract

Sports health is gradually attracting attention, and computer vision technology is integrated into sports health to improve the quality of sports and increase the motivation of athletes. A deep learning sports health video propagation detection and recognition system is built through the mode of video propagation to provide real-time training information for sports and scientific body index parameters and exercise data for sports health programs. An athletics action estimation network (AAEN) is promoted, which initially obtains the correlation features and depth features between human skeleton key points through partial perception units. Then, all the joint point features are classified and correlated based on the affinity field range through the confidence map of the human skeletal node region. All video frames are then fused with similar joint features at the temporal level to extract motion key points in the time scale, and human posture prediction is achieved by fitting between the motion features and the dynamic database. To show the high efficiency of our method, we select three main databases for validation, and the results prove that AAEN outperforms by 13.96%, 16.90%, and 15.10% in precision, F1 score, and recall compared to the SOTA in sports health video detection and recognition. Our method also performs better overall in the same type of algorithms.