2024/07/05 by Han Wang, Wu, Juntao, Wang, Han +12
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Traffic Prediction and Management Techniques #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2407.04277
openalex publication_date 2024/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Pedestrian detection in conventional frame-based imaging often suffers from limited temporal responsiveness and substantial data redundancy. Inspired by the biological retina, event-based vision sensing (EVS) offers ultra-low latency, high temporal resolution, wide dynamic range, and low power consumption, making it highly attractive for pedestrian perception in complex environments. This paper provides a comprehensive review of EVS and its application to pedestrian detection in intelligent transportation and surveillance scenarios. We first summarize the sensing principles, historical development, and key advantages of event-based vision in comparison with conventional frame-based imaging. We then review the major methodological components of event-based pedestrian detection, including sensing inputs, event representations, preprocessing strategies, feature extraction, detection models, datasets, and evaluation metrics. In addition, representative methods are comparatively analyzed in terms of temporal fidelity, detection accuracy, computational efficiency, and deployment complexity. Finally, we discuss the major open challenges in current EB-PD research, including benchmark standardization, event-native model design, multimodal fusion, and real-world deployment, and outline several promising directions for future development. This review aims to provide a structured and up-to-date reference for researchers working on event-based pedestrian perception and related intelligent vision systems.