Multiple Object Tracking via Pixel-level Association

Existing tracking-by-query methods for Multi-Object Tracking rely heavily on the quality of the track query, which is typically generated based on detections. However, detections may contain background and parts of neighboring objects, contaminating the track query and reducing its consistency throughout the tracking propagation. This problem becomes particularly severe under long-term or severe occlusion. To address these issues, we propose PATrack, a pixel-level association framework that constructs the track query from high-quality object masks. Specifically, PATrack suppresses the effects of background features by constructing the track query using objects' masks and reduces contamination of the track query from neighboring objects through the calibration of the objects' masks. It further roughly estimates the occluded regions of object masks and suppresses unreliable trajectory generation for heavily occluded objects, thereby improving robustness to long-term and severe occlusion. PATrack achieves state-of-the-art performance on DanceTrack and SportsMOT, while delivering significant performance on MOT17, demonstrating its extremely high reliability and superiority.