Self-Supervised Multi-Object Tracking with Robust Long-Distance Object Matching

The core message of this paper is that the novel concept of path consistency can be used as a reliable self-supervised signal to train a robust object matching model capable of associating objects over long temporal distances, enabling consistent tracking in the event of occlusion.