A Hybrid Approach for Tracking Individual Players in Broadcast Match Videos
This work provides a fast and accurate tracking solution for sports analytics, but it is incremental as it combines existing models without major breakthroughs.
The paper tackles the problem of tracking individual players in broadcast sports videos, addressing challenges like camera movements and occlusions, and achieves real-time processing at 80 fps on high-definition video with an AUC accuracy of around 0.6, similar to generic state-of-the-art solutions.
Tracking people in a video sequence is a challenging task that has been approached from many perspectives. This task becomes even more complicated when the person to track is a player in a broadcasted sport event, the reasons being the existence of difficulties such as frequent camera movements or switches, total and partial occlusions between players, and blurry frames due to the codification algorithm of the video. This paper introduces a player tracking solution which is both fast and accurate. This allows to track a player precisely in real-time. The approach combines several models that are executed concurrently in a relatively modest hardware, and whose accuracy has been validated against hand-labeled broadcast video sequences. Regarding the accuracy, the tests show that the area under curve (AUC) of our approach is around 0.6, which is similar to generic state of the art solutions. As for performance, our proposal can process high definition videos (1920x1080 px) at 80 fps.