PCA Event-Based Optical Flow: A Fast and Accurate 2D Motion Estimation

For neuromorphic vision sensors such as event-based cameras, a paradigm shift is required to adapt optical flow estimation as it is critical for many applications. Regarding the costly computations, Principal Component Analysis (PCA) approach is adapted to the problem of event-based optical flow est...

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Bibliographic Details
Published in2022 IEEE International Conference on Image Processing (ICIP) pp. 3521 - 3525
Main Authors Khairallah, Mahmoud Z., Bonardi, Fabien, Roussel, David, Bouchafa, Samia
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.10.2022
Subjects
2D motion estimation
Cameras
Estimation
Event-based cameras
Motion estimation
Optical flow
Optical sensors
Principal component analysis
Real-time systems
Vision sensors
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Summary:For neuromorphic vision sensors such as event-based cameras, a paradigm shift is required to adapt optical flow estimation as it is critical for many applications. Regarding the costly computations, Principal Component Analysis (PCA) approach is adapted to the problem of event-based optical flow estimation. We propose different PCA regularization methods enhancing the optical flow estimation efficiently. Furthermore, we show that the variants of our proposed method, dedicated to real-time context, are about two times faster than state-of-the-art implementations while significantly improving optical flow accuracy.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9897875