LAUSR

Entrainment of moving grains in gravel beds has been the subject of many studies to improve the understanding of sediment entrainment. In this paper, following the recent trends to automatically extract the essential information from gravel bed images, a method is proposed based on image processing and object tracking techniques. Experimental data has been collected from a mobile gravel bed placed in a tilting flume. The proper acquired images are used to evaluate the proposed method. Due to a certain degree of shape and intensity similarity between moving and resting grains, some issues can be existed in the detection and tracking steps of moving grain particles. Also, due to different distance of grains from light source and different trajectories of light, gravel bed images usually have non-uniform illumination. This problem can also complicate the detection and tracking steps. Considering all these problems the proposed method is capable of following grains through the majority of their movements, extracting their locations, matching them to their true counterparts in sequential images and estimating the sedim ententrainment statistics closely to their true values with less than 10% error.