Analysis of technical features in basketball video based on deep learning algorithm

Abstract

The research of video-based sports movement analysis technology has an important application value. The introduction of digital video, human–computer interaction and other technologies in sports training can greatly improve training efficiency. This paper studies the technical characteristics of the players in the basketball game video and proposes a behavior analysis method based on deep learning. We first design a method to automatically extract the basketball court and stadium marking line. Subsequently, key frames in the video are captured using a spatiotemporal scoring mechanism. Afterward, we develop a behavior recognition and prediction method based on an encoder–decoder framework. The analysis results can be fed back to coaches and data analysts in real-time to help them analyze the tactics and technical choices. Experiments on the proposed method are carried out on a large basketball video dataset. The results show that the proposed method can effectively identify the motion of video characters while achieving high behavior analysis accuracy.