Vision-based Human-Computer Interaction Utilized in the Robot Control

Human-computer interface (HCI) is very important to improve the efficiency of robot control to support the complex tasks. It is valuable to develop recognition techniques by which compute can be capable of handling humanpsilas continuous gesture effectively. The present work seeks to model human motions in a manner for leaning and recognition. Toward this end,a novel HCI approach based on hidden Markov model(HMM) is proposed to model semantically meaningful human movements by a sequence of camera images. As a maximum likelihood recognizer, HMM is employed to learn each motion class. Finally, experimental results show the performance of the proposed approach with 1000 pictures.