Research, Applications and Prospects of Event-Based Pedestrian Detection: A Survey

TL;DR

This survey comprehensively reviews the development, advantages, challenges, and future prospects of event-based pedestrian detection using bio-inspired sensors, emphasizing applications in autonomous driving.

Contribution

It provides an exhaustive overview of research, datasets, algorithms, and the comparative analysis of event-based versus traditional frame-based pedestrian detection methods.

Findings

Event-based cameras offer low latency and high dynamic range for pedestrian detection.

Event stream processing algorithms are advancing but face challenges in diverse environments.

The review highlights the potential and current limitations of event-based pedestrian detection.

Abstract

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.