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.
Highlights
Sediment transport in fluvial streams is an important topic in hydraulic and environmental engineering [1,2,3]
RGB-D sensors can be used for measuring the surface of dry granular flows [15,16]
The trajectory of sediment particles is the basis for obtaining many important variables in sediment dynamics, such as the number
Summary
Sediment transport in fluvial streams is an important topic in hydraulic and environmental engineering [1,2,3]. Though, it has drawn wide attention for over a century, the physics of bed-load motion at grain scale remains challenging to experimental efforts due to the excessive difficulty in instrumentation in such complex turbulence-particle-bed interaction [4,5,6,7]. Researchers applied the particle tracking velocimetry (PTV) from the flow field measurement community directly to track particles at grain scale [17]. Traditional PTV can identify the speed of particles, but not track the particle individual trajectories. The trajectory of sediment particles is the basis for obtaining many important variables in sediment dynamics, such as the number
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