PCA Event-Based Optical Flow: A Fast and Accurate 2D Motion Estimation
| Autor: | Mahmoud Z. Khairallah, Fabien Bonardi, David Roussel, Samia Bouchafa |
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| Přispěvatelé: | Informatique, BioInformatique, Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 29th IEEE International Conference on Image Processing (ICIP 2022) 29th IEEE International Conference on Image Processing (ICIP 2022), Oct 2022, Bordeaux, France. pp.3521--3525, ⟨10.1109/ICIP46576.2022.9897875⟩ |
| DOI: | 10.1109/ICIP46576.2022.9897875⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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