Soccer Player Tracking in Low Quality Video
This addresses the challenge of player tracking in low-quality video for sports analytics, but it is incremental as it adapts an existing method to new data.
The paper tackles the problem of tracking multiple soccer players in low-quality videos by adapting a state-of-the-art Multiple Object Tracking method, achieving high performance as indicated by conclusive results.
In this paper we propose a system capable of tracking multiple soccer players in different types of video quality. The main goal, in contrast to most state-of-art soccer player tracking systems, is the ability of execute effectively tracking in videos of low-quality. We adapted a state-of-art Multiple Object Tracking to the task. In order to do that adaptation, we created a Detection and a Tracking Dataset for 3 different qualities of video. The results of our system are conclusive of its high performance.