Research on Multi-player Tracking in Soccer Videos by Combining Tracking-Prediction-Detection

Unlike vehicles or pedestrians, which have relatively predictable motion patterns, soccer players try to confuse each other with unexpected changes in velocity or direction and look almost identical because of their same jerseys in each team, and they can be frequently occluded by others. The accuracy of Multi-player tracking, that is the key issue in the soccer video analysis, is highly dependent on the accuracy of the player detection, and some factors such as video quality, camera long-range defocus, noise, weather or environmental changes can also be the factors that make it difficult to accurately detect players. In this paper, we propose an approach that combines the tracking history, query prediction and detection information reasonably to improve the single-view player tracking performance and then integrates the multi-view analysis information to improve the multi-player tracking performance in soccer videos. First, in 2D single-view multi-object tracking, we combine the deep learning-based player detection/tracking result and their prediction information by the trajectory to decrease the bias and mistakes in the player detection and improve the multi-player tracking performance. And, to improve the robustness of object data association, we use an assignment cost calculation method that integrates L2-distance and Intersection over Union (IOU) between the tracking query region and the detected object region, and also use an identity assignment method that combines Hungarian method and greedy one. Then we integrate the multi-view analysis information to decrease the occlusions of the players dramatically and improve the overall tracking performance significantly. Experimental results on several soccer video datasets show that our approach exhibits significant improvements over the previous approaches in terms of Multi-Object Tracking (MOT) performance metrics such as Multi-Object Tracking Accuracy (MOTA), Number of Identity Switchs (IDS) and Multi-Object Tracking Precision (MOTP).