Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Recent Event Camera Innovations: A Survey

Bharatesh Chakravarthi, Aayush Atul Verma, Kostas Daniilidis, Cornelia Fermuller, Yezhou Yang

TL;DR

This survey addresses the problem of consolidating knowledge on event-based vision, outlining how neuromorphic sensors detect changes asynchronously to achieve temporal resolutions far surpassing traditional frames. It presents a structured overview of working principles, hardware models, and performance advantages, followed by milestones, diverse applications, and datasets. It also surveys synthetic data and simulators that enable rigorous testing and reproducible experimentation, and ends with a curated community resource to centralize past work and future directions. The work highlights the practical impact of high-speed, low-bandwidth sensing for robotics, autonomous systems, and real-time perception at scale, aided by standardized benchmarks and simulation tools.

Abstract

Event-based vision, inspired by the human visual system, offers transformative capabilities such as low latency, high dynamic range, and reduced power consumption. This paper presents a comprehensive survey of event cameras, tracing their evolution over time. It introduces the fundamental principles of event cameras, compares them with traditional frame cameras, and highlights their unique characteristics and operational differences. The survey covers various event camera models from leading manufacturers, key technological milestones, and influential research contributions. It explores diverse application areas across different domains and discusses essential real-world and synthetic datasets for research advancement. Additionally, the role of event camera simulators in testing and development is discussed. This survey aims to consolidate the current state of event cameras and inspire further innovation in this rapidly evolving field. To support the research community, a GitHub page (https://github.com/chakravarthi589/Event-based-Vision_Resources) categorizes past and future research articles and consolidates valuable resources.

Recent Event Camera Innovations: A Survey

TL;DR

This survey addresses the problem of consolidating knowledge on event-based vision, outlining how neuromorphic sensors detect changes asynchronously to achieve temporal resolutions far surpassing traditional frames. It presents a structured overview of working principles, hardware models, and performance advantages, followed by milestones, diverse applications, and datasets. It also surveys synthetic data and simulators that enable rigorous testing and reproducible experimentation, and ends with a curated community resource to centralize past work and future directions. The work highlights the practical impact of high-speed, low-bandwidth sensing for robotics, autonomous systems, and real-time perception at scale, aided by standardized benchmarks and simulation tools.

Abstract

Event-based vision, inspired by the human visual system, offers transformative capabilities such as low latency, high dynamic range, and reduced power consumption. This paper presents a comprehensive survey of event cameras, tracing their evolution over time. It introduces the fundamental principles of event cameras, compares them with traditional frame cameras, and highlights their unique characteristics and operational differences. The survey covers various event camera models from leading manufacturers, key technological milestones, and influential research contributions. It explores diverse application areas across different domains and discusses essential real-world and synthetic datasets for research advancement. Additionally, the role of event camera simulators in testing and development is discussed. This survey aims to consolidate the current state of event cameras and inspire further innovation in this rapidly evolving field. To support the research community, a GitHub page (https://github.com/chakravarthi589/Event-based-Vision_Resources) categorizes past and future research articles and consolidates valuable resources.
Paper Structure (12 sections, 5 figures, 6 tables)

This paper contains 12 sections, 5 figures, 6 tables.

Table of Contents

  1. Understanding Event-based Vision -- An Introduction
  2. The Rise of Event-based Vision: A Background
  3. How Event Cameras Work: An Inside Look
  4. Event Camera vs. Frame Camera: A Comparison
  5. Event Camera Models: An Overview
  6. Pioneering Progress: Milestones in Event-based Vision
  7. Event Cameras in Action: Diverse Tasks and Impacts
  8. Data for Innovation: Event-based Vision Datasets
  9. Real-world Datasets
  10. Synthetic Datasets
  11. Simulating Reality: The Event-based Simulators
  12. Conclusion

Figures (5)

  • Figure 1: Publication Trends in Event-based Vision Research.
  • Figure 2: Working Mechanism of Event Cameras: (a) Independent pixel operation converting light into voltage signals for detecting intensity changes. (b) Event generation as a function of logarithmic light intensity over time.
  • Figure 3: Comparison of Frame vs. Event Cameras: The top row shows common issues like motion blur and visibility in frame-based images, while the bottom row shows event-based images with reduced motion blur and better visibility in challenging lighting.
  • Figure 4: Key Milestone Papers and Works in Event-based Vision.
  • Figure 5: Showcasing Broad Applications and Notable Works in Event-based Vision Research.