Optical Flow Estimation using a Correlation Image Sensor based on FlowNet-based Neural Network

Toru Kurihara, Jun Yu


Optical flow estimation is one of a challenging task in computer vision fields. In this paper, we aim to combine correlation image that enables single frame optical flow estimation with deep neural networks. Correlation image sensor captures temporal correlation between incident light intensity and reference signals, that can record intensity variation caused by object motion effectively. We developed FlowNetS-based neural networks for correlation image input. Our experimental results demonstrate proposed neural networks has succeeded in estimating the optical flow.


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