Publication

Abstract : Basketball is a very popular sport, and artificial intelligence (AI) has attracted a lot of attention because it can help with player training, help coaches come up with winning strategies, lessen injuries related to sports, and make the game more enjoyable overall. To precisely track and record player scores in real-time, this article presents an inventive AI-based automated basketball scoring system that makes use of computer vision and deep learning techniques. Using a variety of libraries, including TensorFlow, Fast RCNN, OpenCV, Yolov4, Yolov7, and the mmpose tool, the system uses pose recognition, basketball and hoop detection, and tracking techniques. This technology increases the effectiveness of player training by updating scoreboards and game data on its own. The suggested approach has an average accuracy of 78.74% for identifying objects, 83.75% for detecting shots, and 80% for identifying the shooter. The accuracy of the scoring system in allocating scores to shots is 66.67%. This technology is a significant development in the field of sports analytics, improving the experience of analyzing basketball games and maybe opening up new applications for real-time monitoring situations and other sports. Future research will concentrate on broadcast content analysis and real-time deployment.