Existing nighttime unmanned aerial vehicle (UAV) trackers follow an" Enhance-then-Track"
architecture-first using a light enhancer to brighten the nighttime video, then employing a
daytime tracker to locate the object. This separate enhancement and tracking fails to build
an end-to-end trainable vision system. To address this, we propose a novel architecture
called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night
by efficiently learning to generate darkness clue prompts. Without a separate enhancer …