  Without manually annotated identities, unsupervised multi-object trackers are
inferior to learning reliable feature embeddings. It causes the
similarity-based inter-frame association stage also be error-prone, where an
uncertainty problem arises. The frame-by-frame accumulated uncertainty prevents
trackers from learning the consistent feature embedding against time variation.
To avoid this uncertainty problem, recent self-supervised techniques are
adopted, whereas they failed to capture temporal relations. The interframe
uncertainty still exists. In fact, this paper argues that though the
uncertainty problem is inevitable, it is possible to leverage the uncertainty
itself to improve the learned consistency in turn. Specifically, an
uncertainty-based metric is developed to verify and rectify the risky
associations. The resulting accurate pseudo-tracklets boost learning the
feature consistency. And accurate tracklets can incorporate temporal
information into spatial transformation. This paper proposes a tracklet-guided
augmentation strategy to simulate tracklets' motion, which adopts a
hierarchical uncertainty-based sampling mechanism for hard sample mining. The
ultimate unsupervised MOT framework, namely U2MOT, is proven effective on
MOT-Challenges and VisDrone-MOT benchmark. U2MOT achieves a SOTA performance
among the published supervised and unsupervised trackers.
