Hydrodynamic scene separation from video imagery of ocean wave using autoencoder

Abstract

In this paper, we propose a hydrodynamic scene separation method for wave propagation from video imagery using autoencoder. In the coastal area, image analysis methods such as particle tracking and optical flow with video imagery are usually applied to measure ocean waves owing to some difficulties of direct wave observation using sensors. However, external factors such as ambient light and weather conditions considerably hamper accurate wave analysis in coastal video imagery. The proposed method extracts hydrodynamic scenes by separating only the wave motions through minimizing the effect of ambient light during wave propagation. We have visually confirmed that the separation of hydrodynamic scenes is reasonably well extracted from the ambient light and backgrounds in the two videos datasets acquired from real beach and wave flume experiments. In addition, the latent representation of the original video imagery obtained through the latent representation learning by the variational autoencoder was dominantly determined by ambient light and backgrounds, while the hydrodynamic scenes of wave propagation independently expressed well regardless of the external factors.

Wave-Tracking in the Surf Zone Using Coastal Video Imagery with Deep Neural Networks

“…Before tracking the coastal waves, a deep autoencoder network is established to extract the hydrodynamic scene only, by minimizing the ambient light effect in the coastal video imagery. The proposed model extends to an autoencoder which compresses and reconstructs the input video images by removing the discriminator from the GAN structure and creating natural video images by separating the foreground and background [12].…”