DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking

arXiv:2403.02767v1 Announce Type: new
Abstract: Accurate data association is crucial in reducing confusion, such as ID switches and assignment errors, in multi-object tracking (MOT). However, existing advanced methods often overlook the diversity among trajectories and the ambiguity and conflicts present in motion and appearance cues, leading to confusion among detections, trajectories, and associations when performing simple global data association. To address this issue, we propose a simple, versatile, and highly interpretable data association approach called Decomposed Data Association (DDA). DDA …

abstract advanced arxiv association cs.cv data diversity errors object tracking type