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

Mahmoud Khairallah (1) , Fabien Bonardi (1) , David Roussel (1) , Samia Bouchafa (1)

1 IBISC - Informatique, BioInformatique, Systèmes Complexes

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.

Keywords: Optical flow; Event-based cameras; Principal component analysis; 2D motion estimation

Domains: Signal and Image processing

Mahmoud Khairallah, Fabien Bonardi, David Roussel, Samia Bouchafa. PCA Event-Based Optical Flow: A Fast and Accurate 2D Motion Estimation. 29th IEEE International Conference on Image Processing (ICIP 2022), Oct 2022, Bordeaux, France. pp.3521--3525, ⟨10.1109/ICIP46576.2022.9897875⟩. ⟨hal-03878681⟩