Advertisement

Adaptive εLBP for Background Subtraction

  • LingFeng Wang
  • HuaiYu Wu
  • ChunHong Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

Background subtraction plays an important role in many computer vision systems, yet in complex scenes it is still a challenging task, especially in case of illumination variations. In this work, we develop an efficient texture-based method to tackle this problem. First, we propose a novel adaptive ε LBP operator, in which the threshold is adaptively calculated by compromising two criterions, i.e. the description stability and the discriminative ability. Then, the naive Bayesian technique is adopted to effectively model the probability distribution of local patterns in the pixel level, which utilizes only one single ε LBP pattern instead of ε LBP histogram of local region. Our approach is evaluated on several video sequences against the traditional methods. Experiments show that our method is suitable for various scenes, especially can robust handle illumination variations.

Keywords

Video Sequence Background Subtraction Foreground Object Edge Pixel Illumination Variation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Koller, D., Weber, J., Malik, J.: Robust multiple car tracking with occlusion reasoning. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 189–196. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  2. 2.
    Zhong, J., Sclaroff, S.: Segmenting foreground objects from a dynamic textured background via arobust kalman filter. In: IEEE International Conference on Computer Vision, pp. 44–50 (2003)Google Scholar
  3. 3.
    Oliver, N.M., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 831–843 (2000)CrossRefGoogle Scholar
  4. 4.
    Zhong, J., Sclaroff, S.: Segmenting foreground objects from a dynamic textured background via a robust kalman filter. In: International Conference on Computer Vision, vol. 1, pp. 44–50. IEEE, Los Alamitos (2003)Google Scholar
  5. 5.
    Wren, C.R., Azarbayejani, A., Darrel, T., Pentland, A.: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)CrossRefGoogle Scholar
  6. 6.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)Google Scholar
  7. 7.
    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
  8. 8.
    Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Transaction on Pattern Analysis and Machine Intelligence 28, 657–662 (2006)CrossRefGoogle Scholar
  9. 9.
    Yao, J., Odobez, J.M.: Multi-layer background subtraction based on color and texture. In: IEEE Workshop on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  10. 10.
    Liao, S., Zhao, G., Kellokumpu, V., Pietikainen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
  11. 11.
    Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Statistical modeling of complex backgrounds for foreground object detection. IEEE Transaction on Image Processing 13, 1459–1472 (2004)CrossRefGoogle Scholar
  12. 12.
    Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging In Special Issue on Video Object Processing 11, 172–185 (2005)Google Scholar
  13. 13.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transaction on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  15. 15.
    Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B., Russell, S.: Towards robust automatic traffic scene analysis in real-time. In: IEEE International Conference on Pattern Recognition, vol. 1, pp. 126–131 (1994)Google Scholar
  16. 16.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: IEEE International Conference on Computer Vision, vol. 1, pp. 255–261 (1999)Google Scholar
  17. 17.
    Fisher, R.: The pets 2004 surveillance ground-truth datasets. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance(PETS 2004), vol. 5, pp. 1–5 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • LingFeng Wang
    • 1
  • HuaiYu Wu
    • 2
  • ChunHong Pan
    • 1
  1. 1.NLPR, Institute of AutomationChinese Academy of SciencesChina
  2. 2.Key Laboratory of Machine Perception (MOE)Peking UniversityChina

Personalised recommendations