A Fuzzy Background Modeling Approach for Motion Detection in Dynamic Backgrounds

  • Zhenjie Zhao
  • Thierry Bouwmans
  • Xuebo Zhang
  • Yongchun Fang
Part of the Communications in Computer and Information Science book series (CCIS, volume 346)


Based on Type-2 Fuzzy Gaussian Mixture Model (T2-FGMM) and Markov Random Field (MRF), we propose a novel background modeling method for motion detection in dynamic scenes. The key idea of the proposed approach is the successful introduction of the spatial-temporal constraints into the T2-FGMM by a Bayesian framework. The evaluation results in pixel level demonstrate that the proposed method performs better than the sound Gaussian Mixture Model (GMM) and T2-FGMM in such typical dynamic backgrounds as waving trees and water rippling.


T2-FGMM MRF motion detection dynamic backgrounds 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE Transactions on Image Processing 14(3), 294–307 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)Google Scholar
  3. 3.
    Brutzer, S., Hoferlin, B., Heideman, G.: Evaluation of background subtraction techniques for video surveillance. In: CVPR 2011, pp. 1937–1944 (2011)Google Scholar
  4. 4.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE CVPR 1999 (1999)Google Scholar
  5. 5.
    Zhou, Y., Xu, W., Tao, H., Gong, Y.: Background segmentation using spatial-temporal multi-resolution MRF. In: IEEE Workshops on Application of Computer Vision, vol. 1, pp. 8–13 (2005)Google Scholar
  6. 6.
    Monnet, A., Mittal, A., Paragios, N., Ramesh, V.: Background modeling and subtraction of dynamic scenes. In: CVPR 2003, vol. 2, pp. 1305–1312 (2003)Google Scholar
  7. 7.
    Mahadevan, V., Vansconcelos, N.: Background subtraction in highly dynamic scenes. In: CVPR 2008, pp. 1–6 (2008)Google Scholar
  8. 8.
    El Baf, F., Bouwmans, T., Vachon, B.: Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 772–781. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    El Baf, F.,Bouwmans, T., Vachon, B.: Fuzzy Statistical Modeling of Dynamic Backgrounds for Moving Object Detection in Infrared Videos. In: IEEE CVPR Workshops, pp. 60–65 (2009)Google Scholar
  10. 10.
    Zeng, J., Xie, L., Liu, Z.: Type-2 fuzzy Gaussian mixture models. Pattern Recognition 41(12), 3636–3643 (2008)CrossRefzbMATHGoogle Scholar
  11. 11.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. PAMI 19(7), 780–785 (1997)CrossRefGoogle Scholar
  12. 12.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.S.: Real-time foreground-background segmentation using codebook Model. Real-Time Image 11, 172–185 (2005)CrossRefGoogle Scholar
  13. 13.
    Elgammal, A., Harwood, D., Davis, L.: Non-parametric Model for Background Subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Barnich, O., Van Droogenbroeck, M.: ViBe: A Universal Background Subtraction Algorithm for Video Sequences. Image Processing 20(6), 1709–1724 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Maddalena, L., Petrosino, A.: The SOBS Algorithm: What Are the Limits? In: IEEE Workshop on Change Detection, CVPR 2012 (2012)Google Scholar
  16. 16.
    Bouwmans, T.: Background Subtraction for Visual Surveillance: A Fuzzy Approach. In: Bouwmans, T. (ed.) Handbook on Soft Computing for Video Surveillance, ch. 5. Taylor and Francis Group (2012)Google Scholar
  17. 17.
    Li, S.: Markov Random Field Models in Computer Vision. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 361–370. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  18. 18.
    Zivkovic, Z.: Improved Adaptive Gaussian Mixture Model for Background Subtraction. In: International Conference on Pattern Recognition, vol. 2, pp. 28–31 (2004)Google Scholar
  19. 19.
    IEEE CVPR 2012 Workshops on Change Detection (2012),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhenjie Zhao
    • 1
  • Thierry Bouwmans
    • 2
  • Xuebo Zhang
    • 1
  • Yongchun Fang
    • 1
  1. 1.Institute of Robotics and Automatic Information SystemNankai UniversityChina
  2. 2.Laboratory of Mathematics, Images and ApplicationsUniversity of La RochelleFrance

Personalised recommendations