Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
권호 표지

A Background Subtraction Algorithm for Fence Monitoring Surveillance Systems

담장 감시 시스템을 위한 배경 제거 알고리즘

  • Lee, Bok Ju (Korea University of Technology and Education, School of Computer Science and Engineering) ;
  • Chu, Yeon Ho (Korea University of Technology and Education, School of Computer Science and Engineering) ;
  • Choi, Young Kyu (Korea University of Technology and Education, School of Computer Science and Engineering)
  • 이복주 (한국기술교육대학교 컴퓨터공학부) ;
  • 추연호 (한국기술교육대학교 컴퓨터공학부) ;
  • 최영규 (한국기술교육대학교 컴퓨터공학부)
  • Received : 2015.08.28
  • Accepted : 2015.09.22
  • Published : 2015.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a new background subtraction algorithm for video based fence monitoring surveillance systems is proposed. We adopt the sampling based background subtraction technique and focus on the two main issues: handling highly dynamic environment and handling the flickering nature of pulse based IR (infrared) lamp. Natural scenes from fence monitoring system are usually composed of several dynamic entities such as swaying trees, moving water, waves and rain. To deal with such dynamic backgrounds, we utilize the confidence factor for each background value of the input image. For the flickering IR lamp, the original sampling based technique is extended to handle double background models. Experimental results revealed that our method works well in real fence monitoring surveillance systems.

Keywords

References

  1. Lee, D. E and Choi, Y. K., "Background subtraction algorithm based on Multiple Interval Pixel Sampling," KIPS trans. On Software and Data Engineering. vol. 2, no. 1, pp. 27-34, 2013. https://doi.org/10.3745/KTSDE.2013.2.1.027
  2. McFarlane, N. and Schofield, C., "Segmentation and Tracking of Piglets in Images," Machine Vision Applicaton, vol. 8, pp. 187-193, 1995. https://doi.org/10.1007/BF01215814
  3. Wren, C., Azarbayejani, A., Darrell, T. and Pentland, A., "Pfinder : Real-Time Tracking of the Human Body," IEEE Trans. on PAMI, vol. 19, no. 7, pp. 780-785, July 1997. https://doi.org/10.1109/34.598236
  4. Stauffer, C. and Grimson, W., "Adaptive Background Mixture Models for Real-Time Tracking," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, 1999.
  5. Elgammal, A., Duraiswami, R., Harwood, D. and Davis, L.S., "Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance," Proc. IEEE, vol. 90, no. 7, pp. 1151-1163, 2002. https://doi.org/10.1109/JPROC.2002.801448
  6. Zivkovic, Z., "Improved adaptive gausian mixture model for background subtraction," Proceedings of the International Conference on Pattern Recognition, pp. 28-31, 2004.
  7. Heikkila, M. and Pietikainen, M., "A Texture-Based Method for Modeling the Background and Detecting Moving Objects," IEEE Trans. on PAMI, Vol. 28, No. 4, pp. 657-662, April 2006. https://doi.org/10.1109/TPAMI.2006.68
  8. Wang, H. and Suter, D., "A consensus-based method for tracking : Modelling background scenario and foreground appearance," Pattern Recognition, vol. 40, no. 3, pp. 1091-1105, 2007. https://doi.org/10.1016/j.patcog.2006.05.024
  9. Barnich, O. and Van Droogenbroeck, M., "ViBe : A universal background subtraction algorithm for video sequences," IEEE Trans. on Image Processing, vol. 20, no. 6, pp.1709-1724, 2011. https://doi.org/10.1109/TIP.2010.2101613
  10. Chu, Y. H., Lee, B. J. and Choi, Y. K., "A Video based Traffic Light Recognition System for Intelligent Vehicles," J. of The Korean Society of Semiconductor & Display Equipment Technology, Vol. 14, No. 2, pp. 29-34, 2015.