Intelligent Optimization Algorithm of 3D Tracking Technology in Football Player Moving Image Analysis

Abstract

Computer vision technology began to affect the development of football. There is increasingly high-tech in football broadcast technology, and many application tools have emerged in the field of football broadcast video analysis. The purpose of this paper is to study the improvement of target tracking algorithm for football broadcast video and to study the intelligent optimization algorithm of 3D tracking technology in football player moving image analysis. This paper proposes to select four models of YOLOv5 to perform target detection experiments in football broadcast videos and analyzes the principle of the Deep SORT multitarget tracking algorithm. At the same time, it is based on the 3D tracking and 3D pose estimation of players based on cross-view correlation matching, and to measure the comprehensive performance of the tracker in the football scene, experiments are carried out on the accuracy and speed of the tracker under the football datasets of four different scenes. The experimental results in this paper show that the MOTA values corresponding to the 3D tracking results and 2D projection results obtained in the campus dataset are only 50 and 56.2. This is much lower than the tracking performance when based on other similarity matrices. The MOTA value of the obtained tracking result (92.6) is very close and significantly outperforms other methods. CCOT performs better on datasets 28, 29, 31, and 32, ECO stands out on dataset 38, and Siamese also performs well on datasets 22 and 36.

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