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.

本文提出了一种名为 DarkLighter 的低光图像增强器，采用轻量级的 ME-Net 网络，用于提高无人机跟踪器在夜间、低光环境下的鲁棒性。实验证明，DarkLighter 的普适性和可靠性很高，并已在典型的 UAV 系统上实现，夜间场景的实际测试表明其实用性和可靠性。

DarkLighter：无人机跟踪中的光明之光

DarkLighter: Light Up the Darkness for UAV Tracking

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.

提出了一种具有抗暗功能的新型追踪器 ADTrack，通过在基于相关性过滤器的追踪器中集成高效，有效的低光图像增强器，同时生成基于图像照明变化的目标感知遮罩，以协助上下文过滤器进行强引导追踪。ADTrack 采用双重回归，其中上下文过滤器和目标专注过滤器互相限制进行双重过滤器训练，在 37 个典型的暗场景基准测试中，ADTrack 优于其他同类追踪器，实现了单 CPU 上 34 帧 / 秒的实时速度。