Self-Supervised Multi-Object Tracking with Path Consistency

In this paper, we propose a novel concept of path consis-tency to learn robust object matching without using manual object identity supervision. Our key idea is that, to track a object through frames, we can obtain multiple different as-sociation results from a model by varying the frames it can observe, i.e., skipping frames in observation. As the differ-ences in observations do not alter the identities of objects, the obtained association results should be consistent. Based on this rationale, we generate multiple observation paths, each specifying a different set of frames to be skipped, and formulate the Path Consistency Loss that enforces the as-sociation results are consistent across different observation paths. We use the proposed loss to train our object matching model with only self-supervision. By extensive experiments on three tracking datasets (MOT17, PersonPath22, KITTI), we demonstrate that our method outperforms existing unsu-pervised methods with consistent margins on various eval-uation metrics, and even achieves performance close to su-pervised methods.