Observation Centric and Central Distance Recovery on Athlete Tracking
3rd Place in SportsMOT Challenge on Multi-actor Tracking at DeeperAction Workshop (ECCV 2022)
Hsiang-Wei Huang,
Cheng-Yen Yang,
Jenq-Neng Hwang,
Pyong-Kun Kim,
Kyoungoh Lee,
and Kwang-Ju Kim
In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
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 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-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 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, with achieved HOTA of 73.968 on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset, show the effectiveness of the proposed framework and its robustness in different sports scenes.