  Modern multi-object tracking (MOT) systems usually model the trajectories by
associating per-frame detections. However, when camera motion, fast motion, and
occlusion challenges occur, it is difficult to ensure long-range tracking or
even the tracklet purity, especially for small objects. Although
re-identification is often employed, due to noisy partial-detections, similar
appearance, and lack of temporal-spatial constraints, it is not only unreliable
and time-consuming, but still cannot address the false negatives for occluded
and blurred objects. In this paper, we propose an enhanced MOT paradigm, namely
Motion-Aware Tracker (MAT), focusing more on various motion patterns of
different objects. The rigid camera motion and nonrigid pedestrian motion are
blended compatibly to form the integrated motion localization module.
Meanwhile, we introduce the dynamic reconnection context module, which aims to
balance the robustness of long-range motion-based reconnection, and includes
the cyclic pseudo-observation updating strategy to smoothly fill in the
tracking fragments caused by occlusion or blur. Additionally, the 3D integral
image module is presented to efficiently cut useless track-detection
association connections with temporal-spatial constraints. Extensive
experiments on MOT16 and MOT17 challenging benchmarks demonstrate that our MAT
approach can achieve the superior performance by a large margin with high
efficiency, in contrast to other state-of-the-art trackers.
