Spotting and Recognition for Human-Robot Interaction

Abstract—Visual interpretation of gestures can be useful in accomplishing natural human–robot interaction (HRI). Previous HRI research focused on issues such as hand gestures, sign lan-guage, and command gesture recognition. Automatic recognition of whole-body gestures is required in order for HRI to operate naturally. This presents a challenging problem, because describing and modeling meaningful gesture patterns from whole-body ges-tures is a complex task. This paper presents a new method for recognition of whole-body key gestures in HRI. A human subject is first described by a set of features, encoding the angular rela-tionship between a dozen body parts in 3-D. A feature vector is then mapped to a codeword of hidden Markov models. In order to spot key gestures accurately, a sophisticated method of designing a transition gesture model is proposed. To reduce the states of the transition gesture model, model reduction which merges similar states based on data-dependent statistics and relative entropy is used. The experimental results demonstrate that the proposed method can be efficient and effective in HRI, for automatic recog-nition of whole-body key gestures from motion sequences. Index Terms—Gesture spotting, hidden Markov model (HMM), human–robot interaction (HRI), mobile robot, transition gesture model, whole-body gesture recognition. I