MRF-Based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos

  • Vikas Reddy
  • Conrad Sanderson
  • Andres Sanin
  • Brian C. Lovell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model is almost impossible. In this paper, we propose a method capable of robustly estimating the background and detecting regions of interest in such environments. In particular, we propose to extend the background initialisation component of a recent patch-based foreground detection algorithm with an elaborate technique based on Markov Random Fields, where the optimal labelling solution is computed using iterated conditional modes. Rather than relying purely on local temporal statistics, the proposed technique takes into account the spatial continuity of the entire background. Experiments with several tracking algorithms on the CAVIAR dataset indicate that the proposed method leads to considerable improvements in object tracking accuracy, when compared to methods based on Gaussian mixture models and feature histograms.


Discrete Cosine Transform Gaussian Mixture Model Markov Random Field Training Sequence Foreground Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Reddy, V., Sanderson, C., Sanin, A., Lovell, B.: Adaptive Patch-Based Background Modelling for Improved Foreground Object Segmentation and Tracking. In: Proc. Advanced Video and Signal Based Surveillance (AVSS), pp. 172–179 (2010)Google Scholar
  2. 2.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: Proc. Int. Conf. Computer Vision (ICCV), vol. 1, pp. 255–261 (1999)Google Scholar
  3. 3.
    Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Real-time foreground–background segmentation using codebook model. Real-Time Imaging 11, 172–185 (2005)CrossRefGoogle Scholar
  4. 4.
    Maddalena, L., Petrosino, A.: A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. IEEE Trans. Image Processing 17, 1168–1177 (2008)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, H., Suter, D.: A Novel Robust Statistical Method for Background Initialization and Visual Surveillance. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, pp. 328–337. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Colombari, A., Fusiello, A., Murino, V.: Background Initialization in Cluttered Sequences. In: CVPRW, Washington DC, USA, pp. 197–202 (2006)Google Scholar
  7. 7.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. In: Proc. Intl. Conf. Computer Vision (ICCV), vol. 1, pp. 377–384 (1999)Google Scholar
  8. 8.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 246–252 (1999)Google Scholar
  9. 9.
    Li, L., Huang, W., Gu, I., Tian, Q.: Foreground object detection from videos containing complex background. In: ACM Int. Conf. Multimedia, pp. 2–10 (2003)Google Scholar
  10. 10.
    Gonzales, R., Woods, R.: Digital Image Processing, 3rd edn. Prentice-Hall, Englewood Cliffs (2007)Google Scholar
  11. 11.
    Besag, J.: On the statistical analysis of dirty images. Journal of Royal Statistics Society 48, 259–302 (1986)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Sheikh, Y., Shah, M.: Bayesian Modeling of Dynamic Scenes for Object Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 1778–1792 (2005)CrossRefGoogle Scholar
  13. 13.
    Besag, J.: Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society. Series B (Methodological) 32, 192–236 (1974)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans. Pattern Analysis and Machine Intelligence, 721–741 (1984)Google Scholar
  15. 15.
    Reddy, V., Sanderson, C., Lovell, B.: An efficient and robust sequential algorithm for background estimation in video surveillance. In: Proc. Int. Conf. Image Processing (ICIP), pp. 1109–1112 (2009)Google Scholar
  16. 16.
    Sanderson, C.: Armadillo: An open source C++ linear algebra library for fast prototyping and computationally intensive experiments. Technical report, NICTA (2010)Google Scholar
  17. 17.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Sebastopol (2008)Google Scholar
  18. 18.
    Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP Journal on Image Video Processing (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vikas Reddy
    • 1
    • 2
  • Conrad Sanderson
    • 1
    • 2
  • Andres Sanin
    • 1
    • 2
  • Brian C. Lovell
    • 1
    • 2
  1. 1.NICTASt LuciaAustralia
  2. 2.School of ITEEThe University of QueenslandAustralia

Personalised recommendations