The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Vision-Based Two-Arm Gesture Recognition by Using Longest Common Subsequence

최대 공통 부열을 이용한 비전 기반의 양팔 제스처 인식

  • 최철민 (연세대학교 컴퓨터 과학과 컴퓨터 비전 및 패턴인식 연구실) ;
  • 안정호 (강남대학교 컴퓨터 미디어 공학부) ;
  • 변혜란 (연세대학교 컴퓨터 과학과 컴퓨터 비전 및 패턴인식 연구실)
  • Published : 2008.05.31
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Abstract

In this paper, we present a framework for vision-based two-arm gesture recognition. To capture the motion information of the hands, we perform color-based tracking algorithm using adaptive kernel for each frame. And a feature selection algorithm is performed to classify the motion information into four different phrases. By using gesture phrase information, we build a gesture model which consists of a probability of the symbols and a symbol sequence which is learned from the longest common subsequence. Finally, we present a similarity measurement for two-arm gesture recognition by using the proposed gesture models. In the experimental results, we show the efficiency of the proposed feature selection method, and the simplicity and the robustness of the recognition algorithm.

본 논문은 비전에 기반한 사람의 양팔 제스처의 모델링과 인식에 관한 연구이다. 우리는 양팔 제스처 인식을 위한 특징점의 추출에서부터 제스처의 분류에 이르는 전체적 틀을 제안하였다. 먼저, 양팔 제스처의 모델링을 위해 색채 기반의 양손 추적 방법을 제안하였고, 추출된 양손의 궤적 정보를 효과적으로 선택하게 하는 제스처 구(Phrase) 분석법을 제시하였다. 선택된 특징 점들의 시퀀스(sequence) 들로 이루어진 훈련 데이터들의 최대 공통부열(Longest Common Subsequence) 정보를 이용하여 제스처를 모델링하고 이에 따른 유사도 척도를 제안하였다. 제안된 방법론을 공항 등에서 이용하는 항공기 유도 수신호에 적용하였고, 실험을 통해 제안된 방법론의 효율성과 인식성능을 보였다.

Keywords

References

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