Pedestrian detection remains a critical problem in various domains, such as computer vision, surveillance, and autonomous driving. In particular, accurate and instant detection of pedestrians in low-light conditions and reduced visibility is of utmost importance for autonomous vehicles to prevent accidents and save lives. This paper aims to comprehensively survey various pedestrian detection approaches, baselines, and datasets that specifically target low-light conditions. The survey discusses the challenges faced in detecting pedestrians at night and explores state-of-the-art methodologies proposed in recent years to address this issue. These methodologies encompass a diverse range, including deep learning-based, feature-based, and hybrid approaches, which have shown promising results in enhancing pedestrian detection performance under challenging lighting conditions. Furthermore, the paper highlights current research directions in the field and identifies potential solutions that merit further investigation by researchers. By thoroughly examining pedestrian detection techniques in low-light conditions, this survey seeks to contribute to the advancement of safer and more reliable autonomous driving systems and other applications related to pedestrian safety. Accordingly, most of the current approaches in the field use deep learning-based image fusion methodologies (i.e., early, halfway, and late fusion) for accurate and reliable pedestrian detection. Moreover, the majority of the works in the field (approximately 48%) have been evaluated on the KAIST dataset, while the real-world video feeds recorded by authors have been used in less than six percent of the works.

翻译：行人检测在计算机视觉、监控和自动驾驶等多个领域仍是一个关键问题。特别是在弱光条件和低可见度下，准确、即时地检测行人对于自动驾驶汽车预防事故、挽救生命至关重要。本文旨在全面综述针对弱光条件的各种行人检测方法、基线和数据集。该综述讨论了夜间行人检测面临的挑战，并探索了近年来为应对该问题提出的最先进方法。这些方法涵盖广泛范围，包括基于深度学习、基于特征以及混合方法，在提升挑战性光照条件下的行人检测性能方面展现出有前景的结果。此外，本文强调了该领域当前的研究方向，并识别出值得研究人员进一步探究的潜在解决方案。通过深入审视弱光条件下的行人检测技术，本综述旨在为构建更安全、更可靠的自动驾驶系统及其他与行人安全相关的应用做出贡献。相应地，该领域当前大多数方法采用基于深度学习的图像融合技术（即早期融合、中途融合和晚期融合）以实现准确可靠的行人检测。此外，该领域大部分工作（约48%）已在KAIST数据集上评估，而使用作者录制的真实世界视频流的工作占比不到6%。