Adaptive Model for Object Detection in Noisy and Fast-Varying Environment

  • Dung Nghi Truong Cong
  • Louahdi Khoudour
  • Catherine Achard
  • Amaury Flancquart
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)


This paper presents a specific algorithm for foreground object extraction in complex scenes where the background varies unpredictably over time. The background and foreground models are first constructed by using an adaptive mixture of Gaussians in a joint spatio-color feature space. A dynamic decision framework, which is able to take advantages of the spatial coherency of object, is then introduced for classifying background/foreground pixels. The proposed method was tested on a dataset coming from a real surveillance system including different sensors installed on board a moving train. The experimental results show that the proposed algorithm is robust in the real complex scenarios.


Background subtraction foreground segmentation mixture of Gaussians spatio-color feature space 


  1. 1.
    Elhabian, S.Y., El-Sayed, K.M., Ahmed, S.H.: Moving object detection in spatial domain using background removal techniques - state-of-art. Recent Patents on Computer Science 1(1), 32–54 (2008)CrossRefGoogle Scholar
  2. 2.
    Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)CrossRefGoogle Scholar
  3. 3.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)Google Scholar
  4. 4.
    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
  5. 5.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: International Conference on Image Processing, vol. 5, pp. 3061–3064 (2004)Google Scholar
  6. 6.
    Sheikh, Y., Shah, M.: Bayesian modeling of dynamic scenes for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11), 1778–1792 (2005)CrossRefGoogle Scholar
  7. 7.
    Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 657–662 (2006)CrossRefGoogle Scholar
  8. 8.
    Chen, Y.T., Chen, C.S., Huang, C.R., Hung, Y.P.: Efficient hierarchical method for background subtraction. Pattern Recognition 40(10), 2706–2715 (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Dickinson, P., Hunter, A., Appiah, K.: A spatially distributed model for foreground segmentation. Image and Vision Computing 27(9), 1326–1335 (2009)CrossRefGoogle Scholar
  10. 10.
    Nock, R., Nielsen, F.: Statistical region merging. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1452–1458 (2004)CrossRefGoogle Scholar
  11. 11.
    Von Neumann, J., Burks, A.W.: Theory of Self-Reproducing Automata. University of Illinois Press Champaign, IL (1966)Google Scholar
  12. 12.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dung Nghi Truong Cong
    • 1
  • Louahdi Khoudour
    • 1
  • Catherine Achard
    • 2
  • Amaury Flancquart
    • 1
  1. 1.IFSTTAR, LEOSTVilleneuve d’AscqFrance
  2. 2.UMPC Univ Paris 06, ISIR, UMR 7222France

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