Pedestrian detection has become a cornerstone for several high-level tasks, including autonomous driving, intelligent transportation, and traffic surveillance. There are several works focussed on pedestrian detection using visible images, mainly in the daytime. However, this task is very intriguing when the environmental conditions change to poor lighting or nighttime. Recently, new ideas have been spurred to use alternative sources, such as Far InfraRed (FIR) temperature sensor feeds for detecting pedestrians in low-light conditions. This study comprehensively reviews recent developments in low-light pedestrian detection approaches. It systematically categorizes and analyses various algorithms from region-based to non-region-based and graph-based learning methodologies by highlighting their methodologies, implementation issues, and challenges. It also outlines the key benchmark datasets that can be used for research and development of advanced pedestrian detection algorithms, particularly in low-light situations

翻译：行人检测已成为自动驾驶、智能交通及交通监控等多项高级任务的基础。现有研究多聚焦于日间可见光图像中的行人检测，但在环境光不足或夜间条件下，该任务面临显著挑战。近年来，利用远红外（FIR）温度传感器等替代性数据源在弱光环境下检测行人的新思路得到激发。本研究系统综述了弱光行人检测方法的最新进展，通过强调不同方法的技术原理、实施问题与挑战，对基于区域、非区域及图学习的各类算法进行了系统分类与剖析。同时，本文还梳理了可用于弱光场景下先进行人检测算法研发的关键基准数据集。