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Event-Based Vision: A Survey
Open accessAbstract
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of μs), very high dynamic range (140 dB versus 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.
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“new computer vision algorithms are needed to process the unconventional output of neuromorphic sensors to unlock their potential (Gallego et al., 2022; Sanchez-Garcia et al., 2022).”
From Towards a Smart Bionic Eye : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Gallego et al., 2022
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Event-LSTM: An Unsupervised and Asynchronous Learning-Based Representation for Event-Based Data
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Metadata
- Authors
- Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., & Scaramuzza, D.
- Affiliations
- Einstein Center Digital Future (DE)
- Intel (United States) (US)
- Italian Institute of Technology (IT)
- IBM (United States) (US)
- ETH Zurich (CH)
- Imperial College London (GB)
- University of Pennsylvania (US)
- University of Zurich (CH)
- ORCID
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-01-01
- Volume
- 44
- Issue
- 1
- Pages
- 154-180
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3040838455
- Cited by
- 2,566
- References
- 232
- Citations by year
- 2026: 553 · 2025: 648 · 2024: 510 · 2023: 363 · 2022: 258
- FWCI
- 17.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- SNSF-ERC
- Swiss National Science Foundation
- National Center of Competence in Research Robotics
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › CCD and CMOS Imaging Sensors
- Keywords
- event cameras, event-based vision, bio-inspired sensors, high dynamic range, high temporal resolution, event processing, optical flow, 3D reconstruction, image segmentation, object recognition, spiking neural networks, low-power vision, robot vision
- MeSH terms
- Algorithms, Robotics, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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