Self-Supervised Multi-Object Tracking with Path Consistency
Zijia Lu Bing Shuai Yanbei Chen Zhenlin Xu Davide Modolo
In this paper, we propose a novel concept of path consistency 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
association results from a model by varying the frames it can observe, i.e.,
skipping frames in observation. As the differences 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 association 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 unsupervised methods with consistent margins on various
evaluation metrics, and even achieves performance close to supervised methods.