Exploring Learning-based Motion Models in Multi-Object Tracking

arXiv:2403.10826v1 Announce Type: new
Abstract: 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 …

abstract arxiv become cs.cv dynamic environments filter however limitations linear object prediction sports tracking type