Touched human object segmentation based on Mean-shift algorithm
基于Mean-shift的粘连人体目标分割算法

Human objects Segmentation is one of the key problems of Visual Analysis. In this paper, a novel touched human objects segmentation based on Mean-shift algorithm was proposed. At first, Video Images was preprocessed and motion regions were obtained, and model of human object was built according to statistical characteristics of body surface. Then, a few of points of motion region picked equably were taken as seeds, and local mode centroids were calculated by Mean-shift iterative process. At last, the number of categories was automatically acqured based on the clustering algorithm, and human objects were segmented according to the result of clustering. The experiment based on PETS 2006 Database proves this method is feasible.