Efficient Gesture Interpretation for Gesture-based Human Robot Interaction

Service robots are designed to perform useful services for human-like activities in dynamic and unstructured environments, they will be of great technological and economical importance in the near future. The interactive ability of service robot with human is one of its essential characteristics to implement service tasks by cooperating with people efficiently. In this paper, a vision-based interactive model is presented, it attempts to build a compact and intuitive interaction agent among service robot and common users based on gesture interpretation of human body. The 3-D model of human upper body is proposed for stereo measurement of body parts. Human gestures are estimated by the spatial position of upper body parts. A neural model of human body is generated for gesture segmentation in the training procedure of RCE neural network, it is capable of delineating the color distribution of arbitrary human body’s appearance in color space. The 3-D positions of body parts are acquired by binocular stereo measurement in the segmented area, attentive regions are defined to search the positions of arm joints in the view of active vision. The joint angles of arms are estimated to determine the gesture of human body.