Observation Centric and Central Distance Recovery for Athlete Tracking

Abstract

Multi-Object Tracking on humans has improved rapidly with the development of object detection and re-identification algorithms. However, multi-actor tracking over humans with similar appearance and non-linear 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-player tracking over the sports field, because the players on 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 motion-based 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. Experimental results achieved a HOTA of 73.968 on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset and a HOTA of 49.97 on the McGill HPTDataset, showing the effectiveness of the proposed framework and its robustness in different sports including basketball, football, hockey, and volleyball.