Research on intelligent generation of volleyball training strategies combining YOLOv5 + DeepSORT trajectory data and a key point CNN model

Volleyball training increasingly requires objective information about how players move, coordinate, and execute technical actions during live practice. This study evaluated an integrated video-based system combining YOLOv5 player detection, DeepSORT trajectory tracking, and convolutional neural-network keypoint analysis to support individualized training prescriptions. Fifty-six competitive volleyball athletes were assigned to a six-week parallel-group intervention comparing standard coaching with coaching supported by real-time tactical and biomechanical feedback. The system translated player movement, court positioning, jump mechanics, approach angles, arm-swing timing, and block timing into drill recommendations during training sessions. Compared with standard coaching, the video-supported condition showed greater improvements in transition speed, serve-receive accuracy, rotation latency, out-of-system play reduction, defensive coverage, jump height, take-off time, approach mechanics, arm-swing sequencing, and block timing consistency. Scrimmage indicators, including side-out rate, point differential, and attack efficiency, also improved more in the video-supported group, and several tactical and biomechanical gains were better retained after one to two weeks. The findings indicate that combining real-time trajectory and keypoint information with structured drill selection can support more precise volleyball training decisions. The approach may help coaches identify performance deficits, individualize practice content, and monitor short-term learning, while further validation across venues, hardware conditions, and competition settings remains necessary.