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

ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking