Event-based Solutions for Human-centered Applications: A Comprehensive Review

TL;DR

The paper surveys human-centered applications of event cameras, advocating their superior temporal resolution and privacy-friendly, asynchronous data as a means to surpass frame-based methods in dynamic human tasks. It provides a unified review across body and face domains, detailing event representations, neural architectures, simulators, and datasets, while identifying data compression and privacy challenges as critical adoption hurdles. The work highlights notable progress in gait, action, pose, detection, lip reading, attention to micro-expressions, and gaze analysis, yet emphasizes inconsistent gains relative to RGB baselines and gaps in standardized benchmarks. By outlining future directions—standardized, diverse real-world datasets, hybrid multimodal systems, and architecture- and representation-specific optimizations—the paper aims to accelerate practical deployment of event-based vision for human-centered applications.

Abstract

Event cameras, often referred to as dynamic vision sensors, are groundbreaking sensors capable of capturing changes in light intensity asynchronously, offering exceptional temporal resolution and energy efficiency. These attributes make them particularly suited for human-centered applications, as they capture both the most intricate details of facial expressions and the complex motion dynamics of the human body. Despite growing interest, research in human-centered applications of event cameras remains scattered, with no comprehensive overview encompassing both body and face tasks. This survey bridges that gap by being the first to unify these domains, presenting an extensive review of advancements, challenges, and opportunities. We also examine less-explored areas, including event compression techniques and simulation frameworks, which are essential for the broader adoption of event cameras. This survey is designed to serve as a foundational reference that helps both new and experienced researchers understand the current state of the field and identify promising directions for future work in human-centered event camera applications. A summary of this survey can be found at https://github.com/nmirabeth/event_human

Figures (1)

• Figure 1: Evolution of research focus: Comparing the number of publications on robotics versus human-centered applications of event cameras. Results are based on searches conducted on Google Scholar.