Touched Human Object Segmentation Based on Mean Shift Algorithm

Human objects segmentation is one of key problems of visual analysis. In this paper, a novel touched human objects segmentation based on mean shift algorithm is proposed. At first, video images is preprocessed and foreground objects (BLOB) is obtained, model of human object is built according to statistical characteristics of body surface. Then, a few of points picked equally from BLOB is taken as seeds, and local mode centroids were calculated by mean-shift iterative process. At last, number of categories is automatic acquisition based on clustering algorithm, and human objects is segmentation according to result of clustering. The experiment based on PETS 2006 database prove this method is feasible and precisely.