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

Toru Kurihara, Jun Yu

2020

Abstract

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.

Paper Citation

in Harvard Style

Kurihara T. and Yu J. (2020). Optical Flow Estimation using a Correlation Image Sensor based on FlowNet-based Neural Network. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 847-852. DOI: 10.5220/0009172708470852

in Bibtex Style

@conference{visapp20,
author={Toru Kurihara and Jun Yu},
title={Optical Flow Estimation using a Correlation Image Sensor based on FlowNet-based Neural Network},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={847-852},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009172708470852},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Optical Flow Estimation using a Correlation Image Sensor based on FlowNet-based Neural Network
SN - 978-989-758-402-2
AU - Kurihara T.
AU - Yu J.
PY - 2020
SP - 847
EP - 852
DO - 10.5220/0009172708470852
PB - SciTePress