Research on Application of Computer Vision in Movement Recognition System of Sports Athletes

This manuscript delves into the utilization of computer vision techniques within the motion recognition systems for sports athletes, with a concentrated emphasis on the contributions of stereoscopic vision, human-computer interactive approaches, the least squares methodology, and system emulation within this domain. Stereoscopic vision technology is integrated into the system's architecture to augment the precision of athletes' motion detection. The motion recognition system accomplishes precise identification by seizing and scrutinizing the kinetic attributes of athletes. In this work, the least squares method is additionally deployed to refine the recognition algorithm, thereby enhancing the system's reliability and exactitude. The emulation outcomes demonstrate that the engineered system is capable of delivering precise motion recognition within highly intricate motion scenarios, boasting a recognition accuracy rate of 98.5%. The findings of this research offer dependable technical underpinning for computer-mediated interaction in athletic training, and illustrate the broad potential of stereoscopic vision technology in the recognition of athletes' movements.