UTOPIA: Unconstrained Tracking Objects without Preliminary Examination
  via Cross-Domain Adaptation
Pha Nguyen Kha Gia Quach John Gauch Samee U. Khan Bhiksha Raj Khoa Luu
Multiple Object Tracking (MOT) aims to find bounding boxes and identities of
targeted objects in consecutive video frames. While fully-supervised MOT
methods have achieved high accuracy on existing datasets, they cannot
generalize well on a newly obtained dataset or a new unseen domain. In this
work, we first address the MOT problem from the cross-domain point of view,
imitating the process of new data acquisition in practice. Then, a new
cross-domain MOT adaptation from existing datasets is proposed without any
pre-defined human knowledge in understanding and modeling objects. It can also
learn and update itself from the target data feedback. The intensive
experiments are designed on four challenging settings, including MOTSynth to
MOT17, MOT17 to MOT20, MOT17 to VisDrone, and MOT17 to DanceTrack. We then
prove the adaptability of the proposed self-supervised learning strategy. The
experiments also show superior performance on tracking metrics MOTA and IDF1,
compared to fully supervised, unsupervised, and self-supervised
state-of-the-art methods.