Advertisement

Real-Time Subspace-Based Background Modeling Using Multi-channel Data

  • Bohyung Han
  • Ramesh Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)

Abstract

Background modeling and subtraction using subspaces is attractive in real-time computer vision applications due to its low computational cost. However, the application of this method is mostly limited to the gray-scale images since the integration of multi-channel data is not straightforward; it involves much higher dimensional space and causes additional difficulty to manage data in general. We propose an efficient background modeling and subtraction algorithm using 2-Dimensional Principal Component Analysis (2DPCA) [1], where multi-channel data are naturally integrated in eigenbackground framework [2] with no additional dimensionality. It is shown that the principal components in 2DPCA are computed efficiently by transformation to standard PCA. We also propose an incremental algorithm to update eigenvectors to handle temporal variations of background. The proposed algorithm is applied to 3-channel (RGB) and 4-channel (RGB+IR) data, and compared with standard subspace-based as well as pixel-wise density-based method.

Keywords

Gaussian Mixture Model Background Subtraction Background Modeling Global Illumination Subtraction Algorithm 
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.
    Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional pca: A new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Machine Intell. 26, 131–137 (2004)CrossRefGoogle Scholar
  2. 2.
    Oliver, N.M., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Machine Intell. 22, 831–843 (2000)CrossRefGoogle Scholar
  3. 3.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Anal. Machine Intell. 19, 780–785 (1997)CrossRefGoogle Scholar
  4. 4.
    Friedman, N., Russell, S.: Image segmenation in video sequences: A probabilistic approach. In: Proc. Thirteenth Conf. Uncertainty in Artificial Intell (UAI) (1997)Google Scholar
  5. 5.
    Lee, D.: Effective gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Machine Intell. 27, 827–832 (2005)CrossRefGoogle Scholar
  6. 6.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Fort Collins, CO, 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.
    Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of IEEE 90, 1151–1163 (2002)CrossRefGoogle Scholar
  9. 9.
    Han, B., Comaniciu, D., Davis, L.: Sequential kernel density approximation through mode propagation: Applications to background modeling. In: Asian Conference on Computer Vision, Jeju Island, Korea (2004)Google Scholar
  10. 10.
    Torre, F.D.L., Black, M.: A framework for robust subspace learning. Intl. J. of Computer Vision 54, 117–142 (2003)zbMATHCrossRefGoogle Scholar
  11. 11.
    Kong, H., Wang, L., Teoh, E.K., Li, X., Wang, J.G., Venkateswarlu, R.: Generalized 2d principal component analysis for face image representation and recognition. Neural Networks: Special Issue 5–6, 585–594 (2005)Google Scholar
  12. 12.
    Xu, A., Jin, X., Jiang, Y., Guo, P.: Complete two-dimensional pca for face recognition. In: Int. Conf. Pattern Recognition, Hong Kong, pp. 459–466 (2006)Google Scholar
  13. 13.
    Wang, L., Wang, X., Zhang, X., Feng, J.: The equivalence of two-dimensional pca to line-based pca. Pattern Recognition Letters 26, 57–60 (2005)CrossRefGoogle Scholar
  14. 14.
    Hall, P., Marshall, D., Martin, R.: Merging and splitting eigenspace models. IEEE Trans. Pattern Anal. Machine Intell. 22, 1042–1048 (2000)CrossRefGoogle Scholar
  15. 15.
    Weng, J., Zhang, Y., Hwang, W.: Candid covariance-free incremental principal component analysis. IEEE Trans. Pattern Anal. Machine Intell. 25, 1034–1040 (2003)CrossRefGoogle Scholar
  16. 16.
    Levy, A., Lindenbaum, M.: Sequential karhunen-loeve basis extraction and its application to images. IEEE Trans. Image Process. 9, 1371–1374 (2000)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Bohyung Han
    • 1
  • Ramesh Jain
    • 1
    • 2
  1. 1.Calit2 
  2. 2.School of Information and Computer Sciences, University of California, Irvine, CA 92697USA

Personalised recommendations