Estimation of Water Surface Flow Velocity in Coastal Video Imagery by Visual Tracking with Deep Learning SCIE SCOPUS

Cited 1 time in WEB OF SCIENCE Cited 2 time in Scopus
Title
Estimation of Water Surface Flow Velocity in Coastal Video Imagery by Visual Tracking with Deep Learning
Author(s)
Kim, Jinah; Kim J.
KIOST Author(s)
Kim, Jinah(김진아)
Publication Year
2020-05
Abstract
Phis paper describes the method of flow velocity estimation of water surface in video imagery by tracking waves using deep neural network for visual object tracking with unsupervised learning. The model of deep neural network consists of two stages for scene separation and image registration to extract waves only and track the propagated waves, respectively. The dataset of video imagery acquired at Anmok beach of south Korea has been used to training the model and it learns the behavior of propagated waves. The performance of model is evaluated by measuring image similarity using test dataset. And the estimated flow velocity of water surface in propagated waves is compared with the flow from conventional image processing method of particle image velocity. The results show that our proposed approach with deep learning method is very promising to measure and predict coastal waves especially in the surf zone.
ISSN
0749-0208
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/38648
DOI
10.2112/SI95-101.1
Bibliographic Citation
Journal of Coastal Research, v.95, no.sp1, pp.50 - 54, 2020
Publisher
Coastal Education Research Foundation Inc.
Keywords
Flow velocity; coastal waves; video iruagery; visual tracking; deep learning
Type
Article
Language
English
Document Type
Article
Publisher
Coastal Education Research Foundation Inc.
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