An Eigenbackground Subtraction Method Using Recursive Error Compensation

  • Zhifei Xu
  • Pengfei Shi
  • Irene Yu-Hua Gu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4261)


Eigenbackground subtraction is a commonly used method for moving object detection. The method uses the difference between an input image and the reconstructed background image for detecting foreground objects based on eigenvalue decomposition. In the method, foreground regions are represented in the reconstructed image using eigenbackground in the sense of least mean squared error minimisation. This results in errors that are spread over the entire reconstructed reference image. This will also result in degradation of quality of reconstructed background leading to inaccurate moving object detection. In order to compensate these regions, an efficient method is proposed by using recursive error compensation and an adaptively computed threshold. Experiments were conducted on a range of image sequences with variety of complexity. Performance were evaluated both qualitatively and quantitatively. Comparisons made with two existing methods have shown better approximations of the background images and more accurate detection of foreground objects have been achieved by the proposed method.


Input Image Object Detection Background Image Foreground Object Move Object Detection 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B., Russell, S.: Towards robust automatic traffic scene analysis in real-time. In: Proceedings of the International Conference on Pattern Recognition, Israel, pp. 126–131 (1994)Google 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.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  4. 4.
    Lee, D.S.: Effective gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 827–832 (2005)CrossRefGoogle Scholar
  5. 5.
    Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using non-parametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90(7), 1151–1163 (2002)CrossRefGoogle Scholar
  6. 6.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: ICCV (1), pp. 255–261 (1999)Google Scholar
  7. 7.
    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(8), 831–843 (2000)CrossRefGoogle Scholar
  8. 8.
    Li, Y.: On incremental and robust subspace learning. Pattern Recognition 37, 1509–1518 (2004)MATHCrossRefGoogle Scholar
  9. 9.
    Skočaj, D., Bischof, H., Leonardis, A.: A robust PCA algorithm for building representations from panoramic images. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 761–775. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Skocaj, D., Leonardis, A.: Weighted and robust incremental method for subspace learning. In: ICCV 2003, pp. 1494–1501 (2003)Google Scholar
  11. 11.
    Park, J.-S., Oh, Y.H., Ahn, S.C., Lee, S.-W.: Glasses removal from facial image using recursive error compensation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 805–811 (2005)CrossRefGoogle Scholar
  12. 12.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  13. 13.
    Moghaddam, B., Pentland, A.: Probabilistic visual learning for object detection. In: International Conference on Computer Vision (ICCV 1995), pp. 786–793 (1995)Google Scholar
  14. 14.
    Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Statistical modeling of complex backgrounds for foreground object detection. IEEE Transactions on Image Processing 13(11), 1459–1472 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhifei Xu
    • 1
  • Pengfei Shi
    • 1
  • Irene Yu-Hua Gu
    • 2
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiP.R. China
  2. 2.Department of Signals and SystemsChalmers University of TechnologyGothenburgSweden

Personalised recommendations