한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제17권7호
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  • Pages.45-52
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  • 2012
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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다중 클래스 아다부스트를 이용한 엘리베이터 내 군집 밀도 추정

Crowd Density Estimation with Multi-class Adaboost in elevator

  • 김대훈 (고려대학교 전기전자전파공학과) ;
  • 이영현 (고려대학교 영상정보처리학과) ;
  • 구본화 (고려대학교 영상정보처리학과) ;
  • 고한석 (고려대학교 전기전자전파공학과)
  • Kim, Dae-Hun (Dept. of Electrical Engineering, Korea University) ;
  • Lee, Young-Hyun (Dept. of Visual Information Processing, Korea University) ;
  • Ku, Bon-Hwa (Dept. of Visual Information Processing, Korea University) ;
  • Ko, Han-Seok (Dept. of Electrical Engineering, Korea University)
  • 투고 : 2012.02.01
  • 심사 : 2012.05.16
  • 발행 : 2012.07.31
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초록

본 논문에서는 다중 클래스 아다부스트 기반의 분류기를 이용하여 엘리베이터 내 군집 밀도를 추정하는 방법을 제안한다. SOM을 사용하는 기존의 방법은 재현성이 떨어지며 충분한 성능을 내지 못한다. 제안한 방법은 GLDM(Grey-Level Dependency Matrix)과 GGDM(Grey-Gradient Dependency Matrix)의 텍스처 특징과 다중 클래스 아다부스트 기반의 분류기를 통해 실내 군집 밀도를 추정한다. 다중 클래스를 분류하기 위해 기존의 아다부스트 알고리즘에서 웨이트 업데이트 식을 변형하여 더 높은 성능의 약한 분류기를 생성하도록 하였다. 군집 밀도는 인원수에 따라 0명, 1~2명, 3~4명, 5명 이상 등 네 가지 클래스로 구분하였다. 엘리베이터 내 영상을 이용한 모의 실험 결과 제안된 방법은 기존의 방법보다 약 20% 정도의 검출률 향상을 나타내었다.

In this paper, an crowd density in elevator estimation method based on multi-class Adaboost classifier is proposed. The SOM (Self-Organizing Map) based conventional methods have shown insufficient performance in practical scenarios and have weakness for low reproducibility. The proposed method estimates the crowd density using multi-class Adaboost classifier with texture features, namely, GLDM(Grey-Level Dependency Matrix) or GGDM(Grey-Gradient Dependency Matrix). In order to classify into multi-label, weak classifier which have better performance is generated by modifying a weight update equation of general Adaboost algorithm. The crowd density is classified into four categories depending on the number of persons in the crowd, which can be 0 person, 1-2 people, 3-4 people, and 5 or more people. The experimental results under indoor environment show the proposed method improves detection rate by about 20% compared to that of the conventional method.

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