Andrew C. Freeman
ABSTRACT
The concept of "video" is synonymous with frame-sequence image representations. However, neuromorphic "event" cameras, which are rapidly gaining adoption for computer vision tasks, record frameless video. We believe that these different paradigms of video capture can each benefit from the lessons of the other. To usher in the next era of video systems and accommodate new event camera designs, we argue that we will need an asynchronous, source-agnostic processing pipeline. In this paper, we propose an end-to-end framework for frameless video, and we describe its modularity and amenability to compression and both existing and future applications.
- Andrew C. Freeman, Chris Burgess, and Ketan Mayer-Patel. 2021. Motion Segmentation and Tracking for Integrating Event Cameras. In Proceedings of the 12th ACM Multimedia Systems Conference (Istanbul, Turkey) (MMSys '21). Association for Computing Machinery, New York, NY, USA, 1--11. Google Scholar
Digital Library
- Andrew C. Freeman, Montek Singh, and Ketan Mayer-Patel. 2023. An Asynchronous Intensity Representation for Framed and Event Video Sources. Google ScholarCross Ref
- G. Gallego, T. Delbruck, G. M. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. Davison, J. Conradt, K. Daniilidis, and D. Scaramuzza. 2020. Event-based Vision: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020), 1--1. Google ScholarDigital Library
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- Zhaoqing Pan, Sam Kwong, Yun Zhang, Jianjun Lei, and Hui Yuan. 2014. Fast Coding Tree Unit depth decision for high efficiency video coding. In 2014 IEEE International Conference on Image Processing (ICIP). 3214--3218. Google ScholarCross Ref
- David Tedaldi, Guillermo Gallego, Elias Mueggler, and Davide Scaramuzza. 2016. Feature detection and tracking with the dynamic and active-pixel vision sensor (DAVIS). In 2016 Second International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP). 1--7. Google ScholarCross Ref
Index Terms
- The ADΔER Framework: Tools for Event Video Representations
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