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A practical long-term tracker typically contains three key properties, i.e.an efficient model design, an effective global re-detection strategy and arobust distractor awareness mechanism. However, most state-of-the-art long-termtrackers (e.g., Pseudo and re-detecting based ones) do not take all three keyproperties into account and therefore may either be time-consuming or drift todistractors. To address the issues, we propose a two-task tracking frame work(named DMTrack), which utilizes two core components (i.e., one-shot detectionand re-identification (re-id) association) to achieve distractor-aware fasttracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT)philosophy. To achieve precise and fast global detection, we construct alightweight one-shot detector using a novel dynamic convolutions generationmethod, which provides a unified and more flexible way for fusing targetinformation into the search field. To distinguish the target from distractors,we resort to the philosophy of MOT to reason distractors explicitly bymaintaining all potential similarities' tracklets. Benefited from the strengthof high recall detection and explicit object association, our tracker achievesstate-of-the-art performance on the LaSOT, OxUvA, TLP, VOT2018LT and VOT2019LTbenchmarks and runs in real-time (3x faster than comparisons).

Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy