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Gesture Spotting and Recognition for Human-Robot Interaction.

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Title: Gesture Spotting and Recognition for Human-Robot Interaction.
Authors: Hee-Deok Yang1 hdyang@image.korea.ac.kr, A-yeon Park1, Seong-Whan Lee1 swlee@image.korea.ac.kr
Source: IEEE Transactions on Robotics. Apr2007, Vol. 23 Issue 2, p256-270. 15p. 23 Diagrams.
Subjects: Mobile robots, Human-machine relationship, Hidden Markov models, Robotics, Robot kinematics
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 language, 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 gestures 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 relationship 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 recognition of whole-body key gestures from motion sequences. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
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 language, 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 gestures 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 relationship 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 recognition of whole-body key gestures from motion sequences. [ABSTRACT FROM AUTHOR]
ISSN:15523098
DOI:10.1109/TRO.2006.889491