Gesture spotting and recognition for human-robot interaction
- Authors
- Yang, Hee-Deok; Park, A-Yeon; Lee, Seong-Whan
- Issue Date
- 4월-2007
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- gesture spotting; hidden Markov model (HNM); human-robot interaction (HRI); mobile robot; transition gesture model; whole-body gesture recognition
- Citation
- IEEE TRANSACTIONS ON ROBOTICS, v.23, no.2, pp.256 - 270
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON ROBOTICS
- Volume
- 23
- Number
- 2
- Start Page
- 256
- End Page
- 270
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/125793
- DOI
- 10.1109/TRO.2006.889491
- ISSN
- 1552-3098
- 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.
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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