Hand Motion Design for Performance Enhancement of Vision Based Hand Signal Recognizer

영상기반의 안정적 수신호 인식기를 위한 손동작 패턴 설계 방법

  • Shon, Su-Won (Dept. of Electrical Engineering, Korea University) ;
  • Beh, Joung-Hoon (University of Maryland Institute for Advanced Computer Studies) ;
  • Yang, Cheol-Jong (Dept. of Electrical Engineering, Korea University) ;
  • Wang, Han (Dept. of Electrical Engineering, Korea University) ;
  • Ko, Han-Seok (Dept. of Electrical Engineering, Korea University)
  • 손수원 (고려대학교 전기전자전파공학과) ;
  • 배정훈 ;
  • 양철종 (고려대학교 전기전자전파공학과) ;
  • 왕한 (고려대학교 전기전자전파공학과) ;
  • 고한석 (고려대학교 전기전자전파공학과)
  • Received : 2010.11.24
  • Accepted : 2011.04.02
  • Published : 2011.07.25

Abstract

This paper proposes a language set of hand motions for enhancing the performance of vision-based hand signal recognizer. Based on the statistical analysis of the angular tendency of hand movements in sign language and the hand motions in practical use, we construct four motion primitives as building blocks for basic hand motions. By combining these motion primitives, we design a discernable 'fundamental hand motion set' toward increasing the hand signal recognition. To demonstrate the validity of proposed designing method, we develop a 'fundamental hand motion set' recognizer based on hidden Markov model (HMM). The recognition system showed 99.01% recognition rate on the proposed language set. This result validates that the proposed language set enhances discernaility among the hand motions such that the performance of hand signal recognizer is improved.

본 논문에서는 수신호 인식기에 쓰이기 위한 분별성 있는 손동작을 만드는 방법을 제안한다. 기존의 수화DB에서 손의 움직임을 분석하여 기본 동작이 되는 4가지의 모션 프리미티브를 선정하였으며, 선정된 모션 프리미티브를 조합하여 구별성 있는 '기본 손동작 집합'을 제작하였다. 제안하는 '기본 손동작 집합' 의 구별성을 증명하기 위하여 '기본 손동작 집합' 인식기를 만들고 인식결과를 확인하였다. 사용된 인식기는 hidden Markov model (HMM) 을 기반으로 제작되었다. 기본 손동작 인식 task에 대한 성능평가 결과 99.01%로써 각 모델 간에 높은 구별성을 보이는 것을 확인할 수 있었다.

Keywords

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