Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Determining Method of Factors for Effective Real Time Background Modeling

효과적인 실시간 배경 모델링을 위한 환경 변수 결정 방법

  • Published : 2007.01.15
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Abstract

In the video with a various environment, background modeling is important for extraction and recognition the moving object. For this object recognition, many methods of the background modeling are proposed in a process of preprocess. Among these there is a Kumar method which represents the Queue-based background modeling. Because this has a fixed period of updating examination of the frame, there is a limit for various system. This paper use a background modeling based on the queue. We propose the method that major parameters are decided as adaptive by background model. They are the queue size of the sliding window, the sire of grouping by the brightness of the visual and the period of updating examination of the frame. In order to determine the factors, in every process, RCO (Ratio of Correct Object), REO (Ratio of Error Object) and UR (Update Ratio) are considered to be the standard of evaluation. The proposed method can improve the existing techniques of the background modeling which is unfit for the real-time processing and recognize the object more efficient.

다양한 환경을 포함하고 있는 동영상에서 움직이는 객체를 추출, 인식하기 위해서는 배경 모델링이 중요하다. 이러한 객체 인식을 위한 전처리 과정인 배경 모델링을 위한 여러 방안이 제안되었다. 그중 큐 기반 배경 모델링으로 대표되는 Kumar의 방법이 있다. 하지만 이는 프레임의 갱신검사 주기가 고정되어 있어 여러 시스템에 적용시키는데 한계점이 있다. 본 논문은 큐 기반 배경 모델링 기법을 이용하고 이때 주요한 환경 변수가 되는 슬라이딩 윈도우의 크기 및 영상의 자기 단계에 따른 그룹핑 크기, 프레임의 갱신검사 주기를 배경 모델에 따라 적응적으로 결정하는 방법을 제안한다. 배경 모델에 따른 환경변수를 결정하기 위해 객체 검출율, 객체 오검출율, 갱신율을 평가 기준으로 삼는다. 제안된 방법으로 실시간 처리에 부적합한 기존의 배경 모델링 방법을 개선하여 보다 효과적으로 객체를 인식할 수 있다.

Keywords

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