  Recent years have witnessed the fast evolution and promising performance of
the convolutional neural network (CNN)-based trackers, which aim at imitating
biological visual systems. However, current CNN-based trackers can hardly
generalize well to low-light scenes that are commonly lacked in the existing
training set. In indistinguishable night scenarios frequently encountered in
unmanned aerial vehicle (UAV) tracking-based applications, the robustness of
the state-of-the-art (SOTA) trackers drops significantly. To facilitate aerial
tracking in the dark through a general fashion, this work proposes a low-light
image enhancer namely DarkLighter, which dedicates to alleviate the impact of
poor illumination and noise iteratively. A lightweight map estimation network,
i.e., ME-Net, is trained to efficiently estimate illumination maps and noise
maps jointly. Experiments are conducted with several SOTA trackers on numerous
UAV dark tracking scenes. Exhaustive evaluations demonstrate the reliability
and universality of DarkLighter, with high efficiency. Moreover, DarkLighter
has further been implemented on a typical UAV system. Real-world tests at night
scenes have verified its practicability and dependability.
