Advertisement

Local Spatial Co-occurrence for Background Subtraction via Adaptive Binned Kernel Estimation

  • Bineng Zhong
  • Shaohui Liu
  • Hongxun Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)

Abstract

We present a nonparametric background subtraction method that uses the local spatial co-occurrence correlations between neighboring pixels to robustly and efficiently detect moving objects in dynamic scenes. We first represent each pixel as a joint feature vector consisting of its spatial coordinates and appearance properties (e.g., intensities, color, edges, or gradients). This joint feature vector naturally fuses spatial and appearance features to simultaneously consider meaningful correlation between neighboring pixels and pixels’ appearance changes, which are very important for dynamic background modeling. Then, each pixel’s background model is modeled via an adaptive binned kernel estimation, which is updated by the neighboring pixels’ feature vectors in a local rectangle region around the pixel. The adaptive binned kernel estimation is adopted due to it is computationally inexpensive and does not need any assumptions about the underlying distributions. Qualitative and quantitative experimental results on challenging video sequences demonstrate the robustness of the proposed method.

Keywords

Gaussian Mixture Model Background Subtraction Background Modeling Neighboring Pixel Kernel Density Estimation 
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.
    Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-Time Tracking of the Human Body. TPAMI 19(7), 780–785 (1997)Google Scholar
  2. 2.
    Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity Using Real-Time Tracking. TPAMI 22(8), 747–757 (2000)Google Scholar
  3. 3.
    Javed, O., Shafique, K., Shah, M.: A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information. In: IEEE Workshop on Motion and Video Computing, pp. 22–27 (2002)Google Scholar
  4. 4.
    Bouwmans, T., El Baf, F., Vachon, B.: Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey. Recent Patents on Computer Science 1(3), 219–237 (2008)CrossRefGoogle Scholar
  5. 5.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and Practice of Background Maintenance. In: ICCV, vol. 1, pp. 255–261 (1999)Google Scholar
  6. 6.
    Monnet, A., Mittal, A., Paragios, N., Visvanathan, R.: Background Modeling and Subtraction of Dynamic Scenes. In: ICCV, vol. 2, pp. 1305–1312 (2003)Google Scholar
  7. 7.
    Kato, J., Watanabe, T., Joga, S., Rittscher, J., Blake, A.: An HMM-Based Segmentation Method for Traffic Monitoring Movies. TPAMI 24(9), 1291–1296 (2002)Google Scholar
  8. 8.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-time Surveillance of People and Their Activities. TPAMI 22(8), 809–830 (2000)Google Scholar
  9. 9.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time Foreground-Background Segmentation using Codebook Model. Real-Time Imaging 11(3), 167–256 (2005)CrossRefGoogle Scholar
  10. 10.
    Heikkila, M., Pietikainen, M.: A Texture-Based Method for Modeling the Background and Detecting Moving Objects. TPAMI 28(4), 657–662 (2006)Google Scholar
  11. 11.
    Patwardhan, K.A., Sapiro, G., Morellas, V.: Robust Foreground Detection in Video Using Pixel Layers. TPAMI 30(4), 746–751 (2008)Google Scholar
  12. 12.
    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
  13. 13.
    Sheikh, Y., Shah, M.: Bayesian Modeling of Dynamic Scenes for Object Detection. TPAMI 27(11), 1778–1792 (2005)Google Scholar
  14. 14.
    Mittal, A., Paragios, N.: Motion-based Background Subtraction using Adaptive Kernel Density Estimation. In: CVPR, July 2004, vol. 2, pp. 302–309 (2004)Google Scholar
  15. 15.
    Parag, T., Elgammal, A., Mittal, A.: A Framework for Feature Selection for Background Subtraction. In: CVPR, vol. 2, pp. 1916–1923 (2006)Google Scholar
  16. 16.
    Elgammal, A.M., Duraiswami, R., Davis, L.S.: Efficient Kernel Density Estimation Using the Fast Gauss Transform with Applications to Color Modeling and Tracking. TPAMI 25(11), 1499–1504 (2003)Google Scholar
  17. 17.
    Hall, P., Wand, M.: On the Accuracy of Binned Kernel Estimators. J. Multivariate Analysis (1995)Google Scholar
  18. 18.
    Sain, S.: Multivariate Locally Adaptive Density Estimates. Computational Statistics and Data Analysis (2002)Google Scholar
  19. 19.
    Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Statistical Modeling of Complex Backgrounds for Foreground Object Detection. TIP 13(11), 1459–1472 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bineng Zhong
    • 1
  • Shaohui Liu
    • 1
  • Hongxun Yao
    • 1
  1. 1.Department of Computer Science and EngineeringHarbin Institute of Technology 

Personalised recommendations