Learning Association via Track-Detection Matching for Multi-Object Tracking

ArXi:2512.22105v2 Announce Type: replace Multi-object tracking aims to maintain object identities over time by associating detections across video frames. Two dominant paradigms exist in literature: tracking-by-detection methods, which are computationally efficient but rely on handcrafted association heuristics, and end-to-end approaches, which learn association from data at the cost of higher computational complexity.