Observation Centric and Central Distance Recovery on Sports Player Tracking
This work addresses multi-player tracking in sports, an incremental improvement for sports analytics with domain-specific applications.
The paper tackled the challenge of tracking sports players with similar appearances and nonlinear movements by proposing a motion-based algorithm and post-processing pipelines for basketball, football, and volleyball, achieving a HOTA score of 73.968 and ranking 3rd on the 2022 SportsMOT leaderboard.
Multi-Object Tracking over humans has improved rapidly with the development of object detection and re-identification. However, multi-actor tracking over humans with similar appearance and nonlinear movement can still be very challenging even for the state-of-the-art tracking algorithm. Current motion-based tracking algorithms often use Kalman Filter to predict the motion of an object, however, its linear movement assumption can cause failure in tracking when the target is not moving linearly. And for multi-players tracking over the sports field, because the players in the same team are usually wearing the same color of jersey, making re-identification even harder both in the short term and long term in the tracking process. In this work, we proposed a motionbased tracking algorithm and three post-processing pipelines for three sports including basketball, football, and volleyball, we successfully handle the tracking of the non-linear movement of players on the sports fields. Experiments result on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset demonstrate the effectiveness of our method, which achieves a HOTA of 73.968, ranking 3rd place on the 2022 Sportsmot workshop final leaderboard.