A Depth-Aware Robust Multi-Object Tracker for Crowded Scene by Re-Prioritizing Association Order

Occlusion remains a major challenge in online Multi-Object Tracking (MOT), where existing multi-stage association methods often rely on detection confidence scores despite their weak correlation with occlusion, leading to frequent errors. We propose DARUMA, a depth-aware MOT framework that prioritizes non-occluded objects using occlusion-aware association by re-prioritizing the matching order and refines association with a depth-weighted cost metric for improved robustness in occluded and depth-varying environments. Additionally, we introduce Generic Observation-Centric Momentum (GOCM), which integrates depth-aware velocity estimation and confidence-weighted historical observations to enhance motion modeling. Our method can integrates into existing MOT frameworks, improving association robustness without additional supervision.Extensive evaluations on DanceTrack demonstrate that DARUMA achieves state-of-the-art performance, particularly in complex, occlusion-heavy scenarios.