QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking

Understand

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.