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

被引：1
|
作者：
Kurihara, Toru [1 ]
Yu, Jun [1 ]
机构：
[1] Kochi Univ Technol, 185 Miyanokuchi,Tosayamada Cho, Kami City, Kochi, Japan
来源：
VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP | 2020年
关键词：
Optical Flow; Correlation Image Sensor; Deep Neural Network; FlowNet;
D O I：
10.5220/0009172708470852
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
引用
收藏
页码：847 / 852
页数：6
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