Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
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Fast Object Classification Using Texture and Color Information for Video Surveillance Applications

비디오 감시 응용을 위한 텍스쳐와 컬러 정보를 이용한 고속 물체 인식

  • Islam, Mohammad Khairul (Dept. of Information & Telecommunication Engineering, Korea Aerospace University) ;
  • Jahan, Farah (Dept. of Information & Telecommunication Engineering, Korea Aerospace University) ;
  • Min, Jae-Hong (Dept. of Information & Telecommunication Engineering, Korea Aerospace University) ;
  • Baek, Joong-Hwan (Dept. of Information & Telecommunication Engineering, Korea Aerospace University)
  • Received : 2011.01.31
  • Accepted : 2011.02.28
  • Published : 2011.02.28
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Abstract

In this paper, we propose a fast object classification method based on texture and color information for video surveillance. We take the advantage of local patches by extracting SURF and color histogram from images. SURF gives intensity content information and color information strengthens distinctiveness by providing links to patch content. We achieve the advantages of fast computation of SURF as well as color cues of objects. We use Bag of Word models to generate global descriptors of a region of interest (ROI) or an image using the local features, and Na$\ddot{i}$ve Bayes model for classifying the global descriptor. In this paper, we also investigate discriminative descriptor named Scale Invariant Feature Transform (SIFT). Our experiment result for 4 classes of the objects shows 95.75% of classification rate.

본 논문에서는 텍스쳐와 컬러 정보를 기반으로 비디오 감시를 위한 빠른 물체 분류 방법을 제안한다. 영상들로부터 SURF와 색 히스토그램의 국부적 패치들을 추출하여 그들의 장점을 이용한다. SURF는 명암 내용 정보를 제공하고 색 정보는 패치에 대한 특이성을 증강시킨다. SURF의 빠른 계산뿐만 아니라 객체의 색 정보를 활용한다. 국부적 특징을 이용하여 관심 영역 혹은 영상의 전역적 서술자를 생성하기 위해 Bag of Word 모델을 이용하고, 전역적 서술자를 분류하기 위해 Na$\ddot{i}$ve Bayes 모델을 이용한다. 또한 본 논문에서는 판별적인 기술자인 SIFT도 성능 분석한다. 네 종류의 객체에 대한 실험결과 95.75%의 인식률을 보였다.

Keywords

References

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