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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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Self-citationOptimizing event-driven spiking neural network with regularization and cutoff
Wu, D., Jin, G., Yu, H., Yi, X., & Huang, X. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1522788 · Cited by 16
Privacy-Preserving Visual Localization With Event Cameras
Kim, J., Kim, Y. M., Zahreddine, R. N., Welge, W. A., Krishnan, G., Ma, S., & Wang, J. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3607640 · Cited by 16
Jointly Learning Visual Motion and Confidence from Local Patches in Event Cameras
Kepple, D. R., Lee, D., Prepsius, C., Isler, V., Park, I. M., & Lee, D. D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58539-6_30 · Cited by 16
Asynchronous Spatiotemporal Spike Metric for Event Cameras
Li, J., Fu, Y., Dong, S., Yu, Z., Huang, T., & Tian, Y. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3061122 · Cited by 16
DBA-Filter: A Dynamic Background Activity Noise Filtering Algorithm for Event Cameras
Mohamed, S. A. S., Yasin, J. N., Haghbayan, M.-H., Heikkonen, J., Tenhunen, H., & Plosila, J. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80119-9_44 · Cited by 16
Event Data Downscaling for Embedded Computer Vision
Gruel, A., Martinet, J., Serrano‐Gotarredona, T., & Linares-Barranco, B. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010991900003124 · Cited by 16
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
Chen, J., Wang, Y., Cao, Y., Wu, F. H., & Zha, Z.-J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19906 · Cited by 16
Extracting Unambiguous Drone Signature Using High-Speed Camera
Dizeu, F. B. D., Picard, M., Drouin, M.-A., & Gagné, G. (2022) · IEEE Access · doi:10.1109/access.2022.3170481 · Cited by 16
A toolbox for neuromorphic perception in robotics
Dupeyroux, J., Stroobants, S., & de Croon, G. (2022) · International Conference on Event-based Control, Communication, and Signal Processing · doi:10.1109/ebccsp56922.2022.9845664 · Cited by 16
Synthetic Aperture Imaging with Events and Frames
Wei, L., Zhang, X., Yu, L., Lin, S., Yang, W. T., & Qiao, N. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01721 · Cited by 16
Precise Spiking Motifs in Neurobiological and Neuromorphic Data
Grimaldi, A., Gruel, A., Besnainou, C., Jérémie, J.-N., Martinet, J., & Perrinet, L. (2022) · Brain Sciences · doi:10.3390/brainsci13010068 · Cited by 16
A Reconfigurable Architecture for Real-time Event-based Multi-Object Tracking
Gao, Y., Wang, S., & So, H. K. (2023) · ACM Transactions on Reconfigurable Technology and Systems · doi:10.1145/3593587 · Cited by 16
Asynchronous Event-based Line Tracking for Time-to-Contact Maneuvers in UAS
Eguíluz, A. G., Gómez, J. P. R., Dios, J. R. M., & Ollero, A. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341240 · Cited by 16
Ji, Z., Hu, W., Wang, Z., Yang, K., & Wang, K. (2021) · Sensors · doi:10.3390/s21103558 · Cited by 16
Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs
Hagenaars, J. J. (2020) · Research Repository (Delft University of Technology) · Cited by 16
Bio-inspired optoelectronic devices and systems for energy-efficient in-sensor computing
Wang, X., Huang, H., Tang, J., Hu, R., Du, Y., Wang, Y., Gao, B., He, Q., & Wu, H. (2025) · npj Unconventional Computing · doi:10.1038/s44335-025-00031-7 · Cited by 15
Liu, Z., Sun, Y., Wang, Y., Yang, N., Li, S., & Zhao, X. (2025) · Communications in Transportation Research · doi:10.1016/j.commtr.2025.100202 · Cited by 15
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
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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.
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