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Existing nighttime unmanned aerial vehicle (UAV) trackers follow an"Enhance-then-Track" architecture - first using a light enhancer to brightenthe nighttime video, then employing a daytime tracker to locate the object.This separate enhancement and tracking fails to build an end-to-end trainablevision system. To address this, we propose a novel architecture called DarknessClue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night byefficiently learning to generate darkness clue prompts. Without a separateenhancer, DCPT directly encodes anti-dark capabilities into prompts using adarkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizingand undermining projections for darkness clues. It then injects these learnedvisual prompts into a daytime tracker with fixed parameters across transformerlayers. Moreover, a gated feature aggregation mechanism enables adaptive fusionbetween prompts and between prompts and the base model. Extensive experimentsshow state-of-the-art performance for DCPT on multiple dark scenariobenchmarks. The unified end-to-end learning of enhancement and tracking in DCPTenables a more trainable system. The darkness clue prompting efficientlyinjects anti-dark knowledge without extra modules. Code is available athttps://github.com/bearyi26/DCPT.

DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs