LTTrack: Rethinking the Tracking Framework for Long-Term Multi-Object Tracking

Long-term tracking is a commonly overlooked yet practical scenario in multi-object tracking. Handling occlusion and re-identifying long-lost targets are the main challenges for effective long-term tracking. In occlusion scenarios, both appearance and motion features can be unreliable, leading to association failure. For long-lost targets, predicting their long-term motion suffers from severe error accumulation, making the target re-identification challenging. In this paper, we propose a multi-object tracker called LTTrack for long-term tracking. For occlusion handling, we develop the Position-Based Association (PBA) module, which encodes relative and absolute positions as interaction and motion features for association. With interaction features, PBA can handle occlusion scenes where appearance and motion features are unreliable. For long-lost target re-identification, the Long-Term Motion (LTM) model is devised. By encoding long-term motion trends of targets for long-term motion prediction, LTM alleviates the error accumulation problem. Moreover, to prevent the erroneous deletion of long-lost tracks, we propose the Zombie Track Re-Match (ZTRM) strategy to re-identify long-lost targets so that they will neither be prematurely deleted nor disrupt the association of other tracks. Extensive experiments conducted on MOT17, MOT20, and DanceTrack demonstrate that LTTrack achieves performance comparable to state-of-the-art methods. The code and models are available at https://github.com/Lin-Jiaping/LTTrack.