Event-based cameras, inspired by the biological retina, have evolved into cutting-edge sensors distinguished by their minimal power requirements, negligible latency, superior temporal resolution, and expansive dynamic range. At present, cameras used for pedestrian detection are mainly frame-based imaging sensors, which have suffered from lethargic response times and hefty data redundancy. In contrast, event-based cameras address these limitations by eschewing extraneous data transmissions and obviating motion blur in high-speed imaging scenarios. On pedestrian detection via event-based cameras, this paper offers an exhaustive review of research and applications particularly in the autonomous driving context. Through methodically scrutinizing relevant literature, the paper outlines the foundational principles, developmental trajectory, and the comparative merits and demerits of eventbased detection relative to traditional frame-based methodologies. This review conducts thorough analyses of various event stream inputs and their corresponding network models to evaluate their applicability across diverse operational environments. It also delves into pivotal elements such as crucial datasets and data acquisition techniques essential for advancing this technology, as well as advanced algorithms for processing event stream data. Culminating with a synthesis of the extant landscape, the review accentuates the unique advantages and persistent challenges inherent in event-based pedestrian detection, offering a prognostic view on potential future developments in this fast-progressing field.

翻译：受生物视网膜启发的事件相机已发展成为具有低功耗、微延迟、高时间分辨率和宽动态范围等优势的前沿传感器。当前用于行人检测的相机主要为基于帧的成像传感器，其存在响应迟缓与数据冗余严重的问题。相比之下，事件相机通过摒弃冗余数据传输并消除高速成像场景中的运动模糊，有效克服了这些局限。本文针对基于事件相机的行人检测，特别是在自动驾驶领域的相关研究与应用进行了全面综述。通过系统梳理相关文献，本文阐述了事件检测的基本原理、发展脉络，及其相较于传统基于帧方法的优缺点。本综述深入分析了多种事件流输入及其对应的网络模型，评估了它们在不同操作环境下的适用性。同时，本文探讨了推动该技术发展的关键要素，包括重要数据集与数据采集技术，以及处理事件流数据的先进算法。最后，通过对现有研究格局的综合评述，本文着重分析了基于事件的行人检测所特有的优势与持续存在的挑战，并对这一快速发展领域的未来潜在进展进行了前瞻性展望。