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

Toru Kurihara; Jun Yu

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Paper

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

Topics: Optical Flow and Motion Analyses

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, 847-852, 2020 , Valletta, Malta

Authors: Toru Kurihara and Jun Yu

Affiliation: Kochi University of Technology, 185 Miyanokuchi, Tosayamada-cho, Kami city, Kochi, Japan

Keyword(s): Optical Flow, Correlation Image Sensor, Deep Neural Network, FlowNet.

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.

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Paper citation in several formats:

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; ISSN 2184-4321, SciTePress, pages 847-852. DOI: 10.5220/0009172708470852

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Kurihara, T.
AU - Yu, J.
PY - 2020
SP - 847
EP - 852
DO - 10.5220/0009172708470852
PB - SciTePress