  Multi-object tracking has seen a lot of progress recently, albeit with
substantial annotation costs for developing better and larger labeled datasets.
In this work, we remove the need for annotated datasets by proposing an
unsupervised re-identification network, thus sidestepping the labeling costs
entirely, required for training. Given unlabeled videos, our proposed method
(SimpleReID) first generates tracking labels using SORT and trains a ReID
network to predict the generated labels using crossentropy loss. We demonstrate
that SimpleReID performs substantially better than simpler alternatives, and we
recover the full performance of its supervised counterpart consistently across
diverse tracking frameworks. The observations are unusual because unsupervised
ReID is not expected to excel in crowded scenarios with occlusions, and drastic
viewpoint changes. By incorporating our unsupervised SimpleReID with
CenterTrack trained on augmented still images, we establish a new
state-of-the-art performance on popular datasets like MOT16/17 without using
tracking supervision, beating current best (CenterTrack) by 0.2-0.3 MOTA and
4.4-4.8 IDF1 scores. We further provide evidence for limited scope for
improvement in IDF1 scores beyond our unsupervised ReID in the studied
settings. Our investigation suggests reconsideration towards more
sophisticated, supervised, end-to-end trackers by showing promise in simpler
unsupervised alternatives.
