An Image-Based Uninterrupted Lagrangian Tracking Method for Measurement of Sediment Motion at Grain Scale

Abstract

The imaging technique provides an efficient non-intrusive way for studying local sediment transport with a low rate in open-channel flows. It aims to track all sediment trajectories above the background consists of similar particles (i.e., top-view images of the channel). For this area of interest, currently used imaging methods can be summarized as a two-step framework for identifying and matching active bed-load particles. While effective against a simple and clear background, the two-step approach fails to yield accurate and uninterrupted Lagrangian paths for the sediment particles against complex image background consists of similar particles. This study presents a three-step approach to improve the accuracy of the tracking method. The first two steps of this approach based on image subtraction, centroid exaction and Kalman filter entail improvements to those of the classic methods. The third step based on the nearest neighbor algorithm and spline interpolation manage to identify broken chains and connect them to reconstruct uninterrupted Lagrangian paths of the sediment particles. The verification against simulated images and experimental data shows that the improved three-step approach yields more accurate estimation of bed-load kinematics than the classic two-step method.