Event-Based Vision: A Survey
| Autor: | Guillermo Gallego, Brian Taba, Chiara Bartolozzi, Andrew J. Davison, Kostas Daniilidis, Andrea Censi, Stefan Leutenegger, Davide Scaramuzza, Jörg Conradt, Tobi Delbruck, Garrick Orchard |
|---|---|
| Přispěvatelé: | University of Zurich |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 10009 Department of Informatics Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 000 Computer science knowledge & systems Machine Learning (cs.LG) Computer Science - Robotics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Computer vision High dynamic range Feature detection (computer vision) 10194 Institute of Neuroinformatics Spiking neural network bio-inspired vision low power Pixel Event (computing) business.industry Event cameras asynchronous sensor low latency high dynamic range Applied Mathematics Motion blur Process (computing) Robotics Artificial Intelligence (cs.AI) Computational Theory and Mathematics 570 Life sciences biology 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Neural Networks Computer business Robotics (cs.RO) Software Algorithms |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (1) |
| ISSN: | 0162-8828 1939-3539 |
| Popis: | 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 μ s), 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. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (1) ISSN:0162-8828 ISSN:1939-3539 |
| Databáze: | OpenAIRE |
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