Research on Intelligent Digital Modeling of Sports Games Using Computer Vision Technology

This paper proposes an intelligent digital modeling system based on computer vision technology, aiming to realize real-time analysis and modeling of sports games. This study uses deep learning technology, combined with motion target detection and action recognition algorithms, to build an automated and intelligent sports game modeling system. The system captures game data in real time through video surveillance equipment, and uses target detection algorithms such as YOLO and SSD to identify athletes, balls and other game elements. At the same time, it realizes the accurate capture of athlete movements and tactical intentions based on the algorithm of posture estimation and action recognition. In terms of experimental verification, this paper takes football games as an example. The accuracy of the system in motion target detection is 92.5%, and the accuracy in athlete action recognition is 87.3<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>• In addition, the real-time modeling accuracy of the system is controlled within 5%, and the processing delay is less than 200 milliseconds, which meets the requirements of real-time analysis. Through the analysis of experimental results, this study proves the efficiency and accuracy of the system in complex sports game environments.