  Many multi-object tracking (MOT) methods follow the framework of "tracking by
detection", which associates the target objects-of-interest based on the
detection results. However, due to the separate models for detection and
association, the tracking results are not optimal.Moreover, the speed is
limited by some cumbersome association methods to achieve high tracking
performance. In this work, we propose an end-to-end MOT method, with a Gaussian
filter-inspired dynamic search region refinement module to dynamically filter
and refine the search region by considering both the template information from
the past frames and the detection results from the current frame with little
computational burden, and a lightweight attention-based tracking head to
achieve the effective fine-grained instance association. Extensive experiments
and ablation study on MOT17 and MOT20 datasets demonstrate that our method can
achieve the state-of-the-art performance with reasonable speed.
