Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Object Detection and Classification Using Extended Descriptors for Video Surveillance Applications

비디오 감시 응용에서 확장된 기술자를 이용한 물체 검출과 분류

  • Islam, Mohammad Khairul (Dept. Information & Telecommunication Eng., Korea Aerospace University) ;
  • Jahan, Farah (Dept. Information & Telecommunication Eng., Korea Aerospace University) ;
  • Min, Jae-Hong (Dept. Information & Telecommunication Eng., Korea Aerospace University) ;
  • Baek, Joong-Hwan (Dept. Information & Telecommunication Eng., Korea Aerospace University)
  • Received : 2010.12.17
  • Accepted : 2011.05.09
  • Published : 2011.07.25
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Abstract

In this paper, we propose an efficient object detection and classification algorithm for video surveillance applications. Previous researches mainly concentrated either on object detection or classification using particular type of feature e.g., Scale Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) etc. In this paper we propose an algorithm that mutually performs object detection and classification. We combinedly use heterogeneous types of features such as texture and color distribution from local patches to increase object detection and classification rates. We perform object detection using spatial clustering on interest points, and use Bag of Words model and Naive Bayes classifier respectively for image representation and classification. Experimental results show that our combined feature is better than the individual local descriptor in object classification rate.

본 논문은 비디오 감시 장치에 사용되는 효율적인 물체 검출 및 분류 알고리즘을 제안한다. 이전 연구는 주로 Scale Invariant Feature Transform (SIFT)나 Speeded Up Robust Feature (SURF)와 같은 특정 형태의 특징을 이용해 물체를 검출하거나 분류하였다. 본 논문에서는 물체 검출 및 분류에 상호 작용하는 알고리즘을 제안한다. 이는 로컬 패치들로부터 얻어지는 텍스쳐나 컬러 분포 같은 서로 다른 특성을 갖는 특징값을 이용해 물체의 검출 및 분류율을 높인다. 물체 검출에는 특징점들의 공간적인 클러스터링을, 이미지 표현이나 분류에는 Bag of Words 모델과 Naive Bayes 분류기를 사용한다. 실험을 통해 제안한 기법이 로컬 기술자를 사용한 물체 분류기법보다 우수한 성능을 나타냄을 보인다.

Keywords

References

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