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

Kim, J. and Kim, J., 2020. Estimation of water surface flow velocity using coastal video imagery by visual tracking with deep learning. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 522-526. Coconut Creek (Florida), ISSN 0749-0208. This 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.