Design and Application of Intelligent Teaching and Training System for College Basketball Based on 3D Motion Recognition Technology

Abstract

Abstract Action recognition based on 3D technology is showing a trend of universal application in the field of kinesiology day by day. This paper utilizes 3D action recognition technology to develop an auxiliary teaching system suitable for college basketball teaching and training. The top-down method is used for human pose estimation to provide human skeleton characterization and shield obstacles such as background and illumination for action recognition technology. A new edge connection is added to the original graph convolution algorithm to form the human action recognition algorithm in this paper, and a spatial attention module is introduced to improve the accuracy of model action recognition. The system is equipped with two significant algorithms for human posture estimation and action recognition, and the final design is finished as an auxiliary system for college basketball teaching and training. The system can match the similarity between students’ movements and standard movements with a minimum of more than 80%, and the basketball scores of students are generally improved to more than 90 points after using the system.