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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.13627 |
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| _version_ | 1866929476481318912 |
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| author | Chakravarthi, Bharatesh Verma, Aayush Atul Daniilidis, Kostas Fermuller, Cornelia Yang, Yezhou |
| author_facet | Chakravarthi, Bharatesh Verma, Aayush Atul Daniilidis, Kostas Fermuller, Cornelia Yang, Yezhou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13627 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent Event Camera Innovations: A Survey Chakravarthi, Bharatesh Verma, Aayush Atul Daniilidis, Kostas Fermuller, Cornelia Yang, Yezhou Computer Vision and Pattern Recognition 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. |
| title | Recent Event Camera Innovations: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.13627 |