Research on Soccer Player Tracking Algorithm Based on Deep Learning

Target tracking technology is of great significance in football game video, and is the basis of high-level semantic tasks such as video summary generation, player motion analysis, game strategy formulation and football event detection. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly including correlation filtering and deep learning, but none of them can achieve high accuracy player tracking for soccer game video.In recent years, AI and computer vision have become hot topics, attracted the close attention of a large number of experts and researchers, and triggered an upsurge of extensive and in-depth research. This paper studies the soccer player tracking algorithm based on deep learning. A football player tracking scheme based on deep learning is proposed: a convolutional neural network is built to extract the rich visual features of players in football game video, and the network is trained on a large number of data sets containing similar objects, which improves the ability of the algorithm to identify the same team members. The main objective of the project is to develop a system that can track the position and movement of players in real time, which will be used for live broadcast purposes. In order to achieve this goal, we designed a system using deep learning technology and computer vision algorithm. We also use some advanced technologies, such as 3D graphics processing unit (GPU) and field programmable gate array (FPGA).