바디 제스처 인식을 위한 기초적 신체 모델 인코딩과 선택적 / 비동시적 입력을 갖는 병렬 상태 기계 Primitive Body Model Encoding and Selective / Asynchronous Input-Parallel State Machine for Body Gesture Recognition

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Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding,which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data,while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.
Publisher
한국로봇학회
Issue Date
2013-03
Language
Korean
Citation

로봇학회 논문지, v.8, no.1, pp.1 - 7

ISSN
1975-6291
URI
http://hdl.handle.net/10203/202749
Appears in Collection
EE-Journal Papers(저널논문)
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