Multiple Player Tracking With 3D Projection and Spatio-Temporal Information In Multi-View Sports Videos

Abstract

Player tracking is a fundamental task in sports video understanding. Many technical challenges should be addressed due to irregular movement, occlusion between players, and complex background. In this work, we present a framework that utilizes synchronized videos captured from multiple view-points. We construct 2D player trajectories from each video, construct 3D player trajectories based on multiple videos, and then associate 2D and 3D trajectories to achieve multiple player tracking. Experimental results show that the proposed method achieves SOTA performance on a volleyball dataset and a basketball dataset.