  Similarity learning has been recognized as a crucial step for object
tracking. However, existing multiple object tracking methods only use sparse
ground truth matching as the training objective, while ignoring the majority of
the informative regions in images. In this paper, we present Quasi-Dense
Similarity Learning, which densely samples hundreds of object regions on a pair
of images for contrastive learning. We combine this similarity learning with
multiple existing object detectors to build Quasi-Dense Tracking (QDTrack),
which does not require displacement regression or motion priors. We find that
the resulting distinctive feature space admits a simple nearest neighbor search
at inference time for object association. In addition, we show that our
similarity learning scheme is not limited to video data, but can learn
effective instance similarity even from static input, enabling a competitive
tracking performance without training on videos or using tracking supervision.
We conduct extensive experiments on a wide variety of popular MOT benchmarks.
We find that, despite its simplicity, QDTrack rivals the performance of
state-of-the-art tracking methods on all benchmarks and sets a new
state-of-the-art on the large-scale BDD100K MOT benchmark, while introducing
negligible computational overhead to the detector.
