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.

基于事件的相机在行人检测领域中的应用研究及发展现状的详细回顾，包括基本原理、相对传统帧图像方法的优势与劣势、多种事件流输入和相应网络模型的应用性评估、数据集和数据获取技术以及处理事件流数据的高级算法。同时，对基于事件的行人检测的独特优势和持续挑战进行了综述，并对未来发展进行了前瞻性展望。

事件驱动行人检测研究、应用与前景：综述