Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Gesture Recognition Method using Tree Classification and Multiclass SVM

다중 클래스 SVM과 트리 분류를 이용한 제스처 인식 방법

  • Oh, Juhee (Dept. of Imaging Science and Arts, GSAIM, Chung-Ang University) ;
  • Kim, Taehyub (Dept. of Imaging Science and Arts, GSAIM, Chung-Ang University) ;
  • Hong, Hyunki (Dept. of Imaging Science and Arts, GSAIM, Chung-Ang University)
  • 오주희 (중앙대학교 첨단영상대학원 영상학과) ;
  • 김태협 (중앙대학교 첨단영상대학원 영상학과) ;
  • 홍현기 (중앙대학교 첨단영상대학원 영상학과)
  • Received : 2013.03.20
  • Published : 2013.06.25
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Abstract

Gesture recognition has been widely one of the research areas for natural user interface. This paper presents a novel gesture recognition method using tree classification and multiclass SVM(Support Vector Machine). In the learning step, 3D trajectory of human gesture obtained by a Kinect sensor is classified into the tree nodes according to their distributions. The gestures are resampled and we obtain the histogram of the chain code from the normalized data. Then multiclass SVM is applied to the classified gestures in the node. The input gesture classified using the constructed tree is recognized with multiclass SVM.

제스처 인식은 자연스러운 사용자 인터페이스를 위해 활발히 연구되는 중요한 분야이다. 본 논문에서는 키넥트 카메라로부터 입력되는 사용자의 3차원 관절(joint) 정보를 해석하여 제스처를 인식하는 방법이 제안된다. 대상으로 하는 제스처의 분포 특성에 따라 분류 트리를 설계하고 입력 패턴을 분류한다. 그리고 제스처를 리샘플링 및 정규화 하여 일정한 구간으로 나누고 각 구간의 체인코드 히스토그램을 추출한다. 트리의 각 노드별로 분류된 제스처에 다중 클래스 SVM(Multiclass Support Vector Machine)를 적용하여 학습한다. 이후 입력 데이터를 구성된 트리로 분류한 다음, 학습된 다중 클래스 SVM을 적용하여 제스처를 분류한다.

Keywords

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