  A practical long-term tracker typically contains three key properties, i.e.
an efficient model design, an effective global re-detection strategy and a
robust distractor awareness mechanism. However, most state-of-the-art long-term
trackers (e.g., Pseudo and re-detecting based ones) do not take all three key
properties into account and therefore may either be time-consuming or drift to
distractors. To address the issues, we propose a two-task tracking frame work
(named DMTrack), which utilizes two core components (i.e., one-shot detection
and re-identification (re-id) association) to achieve distractor-aware fast
tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT)
philosophy. To achieve precise and fast global detection, we construct a
lightweight one-shot detector using a novel dynamic convolutions generation
method, which provides a unified and more flexible way for fusing target
information into the search field. To distinguish the target from distractors,
we resort to the philosophy of MOT to reason distractors explicitly by
maintaining all potential similarities' tracklets. Benefited from the strength
of high recall detection and explicit object association, our tracker achieves
state-of-the-art performance on the LaSOT, OxUvA, TLP, VOT2018LT and VOT2019LT
benchmarks and runs in real-time (3x faster than comparisons).
