Human activity analysis is receiving increasing attention from computer vision researchers. One challenge is the segmentation of human body into meaningful body parts. A semi supervised clustering based body parts segmentation algorithm of 3D reconstructed human is presented in this paper. Firstly

we segment human body parts with the help of posture parameters of the previous frame. Then the structure information of human body is adopted to classify some points and initialize the centers of the semi supervised clustering. Finally

based on the shape of body parts

semi supervised clustering method is used to segment the body parts. In addition

body posture parameters are estimated with the segmentation result of body parts. The system is validated with IXMAS database

which includes 6 actors and 6 kinds of activity. The experimental results show that the presented algorithm can adapt the variety of views

actors and activitiesy.