ClarityTrack for multi object tracking via hierarchical association and environment specific cost matching

Existing Multi-Object Tracking methods have predominantly relied on strategies that combine motion and appearance costs at fixed ratios. Although the reliability of each cue varies significantly depending on scene conditions, this fixed-weight approach fails to fully reflect the diverse tracking situations within a frame, leading to association errors and trajectory fragmentation. To overcome these limitations, we propose ClarityTrack, an environment-aware rule-based system. ClarityTrack comprises three core modules. Balanced Cascade Association adopts a detection confidence-based two-stage hierarchical structure, performing balanced 50:50 fusion of motion and appearance cues for high-quality detections in the first stage, and conservative matching using only motion cues for low-quality detections in the second stage. Condition-Aware Matching with Weights defines pre-optimized parameter sets for each environment and conditionally selects them based on the quality of individual track-detection pairs. Motion-Appearance Consistency Check cross-validates the consistency between motion predictions and appearance similarity to suppress inconsistent associations, effectively suppressing mismatches in non-linear motion environments. The experimental results demonstrate that ClarityTrack achieves competitive HOTA, IDF1, and AssA on the MOT17, MOT20, and DanceTrack datasets. In contrast to existing trackers that rely on fixed-weight fusion, ClarityTrack simultaneously secures tracking quality and interpretability through an explicit framework that pre-optimizes parameters to reflect the environmental characteristics of the target domain and conditionally switches them to suit individual matching situations.