Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
권호 표지

Implementation of a Robust Visual Surveillance Algorithm under outdoor environment

옥외 환경에강인한 영상 감시알고리듬구현

  • Published : 2009.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes a robust visual surveillance algorithm under outdoor environment. One of the difficult problems for outdoor is to obtain effective updating process of background images. Because background images generally contain the shadows of buildings, trees, moving clouds and other objects, they are changed by lapse of time and variation of illumination. They provide the lowering of performance for surveillance system under outdoor. In this paper, a robust algorithm for visual surveillance system under outdoor is proposed, which apply the mixture Gaussian filter and color invariant property on pixel level to update background images. In results, it was showed that the moving objects can be detected on various shadows under outdoor.

본 논문에서는 옥외 환경에 강인한 영상 감시알고리듬을 구현하는 과정을 기술하였다. 옥외 감시시스템의 어려운 처리 과정들 중 하나는 배경화면을 효과적으로 갱신하는 것이다. 배경 영상에는 건물, 나무들, 이동하는 구름 및 기타 다른 물체들의 그림자를 포함하기 때문에. 시간과 조명광에 따라 변화한다. 이는 옥외에서의 감시시스템의 성능을 저하시킨다. 따라서 본 논문에서는 배경 영상을 효과적으로 갱신하기 위해 적응 혼합 가우시안 필터와 컬러불변성을 화소레벨에서 적용하여 옥외에서도 강인한 영상 감시알고리듬을 제안하였다. 그 결과, 다양한 그림자가 있는 옥외에서 움직이는 대상 물체를 검출할 수 있음을 확인하였다.

Keywords

References

  1. W.E.L. Grimson C. Staffer R.Romano L.Lee "Using adaptive tracking to classify and monitor activities in a site" IEEE, 1998.
  2. Chris Stauffer, W.E.L Grimson "Adaptive background mixture models for real-time tracking" IEEE,1999.
  3. Kenji Irie,Alan E.McKinnon, Keith Unsworth, Ian M. Woodhead "Shadow Removal for Object Tracking in Complex Outdoor Scenes", Proceedings of Image and Vision Computing New Zealand 2007, pp.25-30, Hamilton, New Zealnd, December 2007.
  4. 이호정, 김영태, 김희수, 배태면, 하영호, “컬러 히스토그램 기반 영상 검색을 위한 효율적인 컬러 특징 정보 추출 기법” 한국 통신 학회 논문지 Vo1.25 No.8B, 2000.
  5. Constantin Vertan, Nozha Boujemaa, "Color Texture Classification by Normalized Color Space Representation" IEEE 1999.
  6. K. Irie, A. E. McKinnon, K. Unsworth, and I.M. Woodhead, "A technique for evaluation of CCD video-camera noise (Accepted forpublication)," IEEE Trans. Circuits and Systems for Video Technology, to bepublished, 2007.
  7. Gunter Wyszecki "Color Science : Concepts and Methods, Quantitative Data and Formulae, 2nd Edition", A Wiley-Interscience Publication, 1982.
  8. G.L. Foresti, C.SRegazzoni, and R. Visvanathan, "Scanning the issue/technology-Special issue on video communications, processing and understanding for third generation surveillance systems,"Proc. IEEE, vol. 89. 10,pp.1355-1367, Oct.2001.
  9. J.N. Kapur, P.K. Sahoo, and A.K.C. Wong, "A new method for gray-level picture thresholding using the entropy of the histogram," Computer Vision, Craphics, Image Processing, vol. 29, no.3, pp.273-285, 1985. https://doi.org/10.1016/0734-189X(85)90125-2
  10. N.Otsu, "A threshold selection method from gray level histograms," IEEE Trans. Syst., Man, Cybern., vol. 9, no. 1, pp.62-66, 1979.
  11. T.W. Ridler and S. Calvard, "Picture thresholding using an iterative selection method,"IEEE Trans. Syst., Man, Cybern., vol.8, no.8 ,pp.630-632, 1978. https://doi.org/10.1109/TSMC.1978.4310039
  12. P.L. Rosin, "Thresholding for change detection," Comput. Vis. Image Understanding, vol. 86, no.2, pp.79-95, 2002. https://doi.org/10.1006/cviu.2002.0960
  13. L. Snidaro and G.L. Foresti, "Real-time thresholding with Euler numbers," Pattern Recognit. Lett., vol.24, no.9-10, pp.1533-1544, June 2003. https://doi.org/10.1016/S0167-8655(02)00392-6