  Prior correlation filter (CF)-based tracking methods for unmanned aerial
vehicles (UAVs) have virtually focused on tracking in the daytime. However,
when the night falls, the trackers will encounter more harsh scenes, which can
easily lead to tracking failure. In this regard, this work proposes a novel
tracker with anti-dark function (ADTrack). The proposed method integrates an
efficient and effective low-light image enhancer into a CF-based tracker.
Besides, a target-aware mask is simultaneously generated by virtue of image
illumination variation. The target-aware mask can be applied to jointly train a
target-focused filter that assists the context filter for robust tracking.
Specifically, ADTrack adopts dual regression, where the context filter and the
target-focused filter restrict each other for dual filter learning. Exhaustive
experiments are conducted on typical dark sceneries benchmark, consisting of 37
typical night sequences from authoritative benchmarks, i.e., UAVDark, and our
newly constructed benchmark UAVDark70. The results have shown that ADTrack
favorably outperforms other state-of-the-art trackers and achieves a real-time
speed of 34 frames/s on a single CPU, greatly extending robust UAV tracking to
night scenes.
