Video Imagery-Based Coastal Wave Tracking for Ocean Observation in Surf Zone Using Deep Convolutional Neural Network

To observe coastal waves in the surf zone, we develop a deep coastal wave tracking model using video imagery and apply it to Samcheok Beach on the east coast of Korea, which has a serious coastal erosion problem. A deep learning-based non-rigid image registration method is applied to detect spatial changes in waves at specific time intervals from a consecutive video image sequence and track their displacement. Through this displacement, the velocity of the propagated wave is estimated. For accurate detection of waves and tracking spatial changes, a preprocessing step is included in which only the sea area, excluding waves in the video imagery. The results of wave tracking are visually examined, using particles to see whether they follow the wave’s crest and white foam well, and it shows that the proposed method is qualitatively feasible for wave observation in a surf zone.