  The main challenge of Multi-Object Tracking~(MOT) lies in maintaining a
continuous trajectory for each target. Existing methods often learn reliable
motion patterns to match the same target between adjacent frames and
discriminative appearance features to re-identify the lost targets after a long
period. However, the reliability of motion prediction and the discriminability
of appearances can be easily hurt by dense crowds and extreme occlusions in the
tracking process. In this paper, we propose a simple yet effective multi-object
tracker, i.e., MotionTrack, which learns robust short-term and long-term
motions in a unified framework to associate trajectories from a short to long
range. For dense crowds, we design a novel Interaction Module to learn
interaction-aware motions from short-term trajectories, which can estimate the
complex movement of each target. For extreme occlusions, we build a novel
Refind Module to learn reliable long-term motions from the target's history
trajectory, which can link the interrupted trajectory with its corresponding
detection. Our Interaction Module and Refind Module are embedded in the
well-known tracking-by-detection paradigm, which can work in tandem to maintain
superior performance. Extensive experimental results on MOT17 and MOT20
datasets demonstrate the superiority of our approach in challenging scenarios,
and it achieves state-of-the-art performances at various MOT metrics.
