Self-Supervised Multi-Object Tracking with Path Consistency

In this paper, we propose a novel concept of path consistency to learn robust object association without using the manual object identity supervision. Our key idea is that, to track a object through frames, we can obtain multiple different association results from a model by varying the frames it can observe, i.e., skipping frames in observation. As the difference in observations does not alter the identities of objects, the obtained association results should be consistent. Based on this rationale, we generate multiple paths by skipping observations in intermediate frames and formulate the Path Consistency Loss that enforces the association results are consistent with those different observation paths. We train an object matching model with the proposed loss, and with extensive experiments on three tracking datasets (MOT17, PersonPath22, KITTI), we demonstrate that our method outperforms existing unsupervised methods with consistent margins on various evaluation metrics, and even achieves performance close to supervised methods.