Exploring Learning-based Motion Models in Multi-Object Tracking
Hsiang-Wei Huang Cheng-Yen Yang Wenhao Chai Zhongyu Jiang Jenq-Neng Hwang
In the field of multi-object tracking (MOT), traditional methods often rely
on the Kalman Filter for motion prediction, leveraging its strengths in linear
motion scenarios. However, the inherent limitations of these methods become
evident when confronted with complex, nonlinear motions and occlusions
prevalent in dynamic environments like sports and dance. This paper explores
the possibilities of replacing the Kalman Filter with various learning-based
motion model that effectively enhances tracking accuracy and adaptability
beyond the constraints of Kalman Filter-based systems. In this paper, we
proposed MambaTrack, an online motion-based tracker that outperforms all
existing motion-based trackers on the challenging DanceTrack and SportsMOT
datasets. Moreover, we further exploit the potential of the state-space-model
in trajectory feature extraction to boost the tracking performance and proposed
MambaTrack+, which achieves the state-of-the-art performance on DanceTrack
dataset with 56.1 HOTA and 54.9 IDF1.