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A PCA-Based Technique to Detect Moving Objects

  • Nicolas Verbeke
  • Nicole Vincent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

Moving objects detection is a crucial step for video surveillance systems. The segmentation performed by motion detection algorithms is often noisy, which makes it hard to distinguish between relevant motion and noise motion. This article describes a new approach to make such a distinction using principal component analysis (PCA), a technique not commonly used in this domain. We consider a ten-frame subsequence, where each frame is associated with one dimension of the feature space, and we apply PCA to map data in a lower-dimensional space where points picturing coherent motion are close to each other. Frames are then split into blocks that we project in this new space. Inertia ellipsoids of the projected blocks allow us to qualify the motion occurring within the blocks. The results obtained are encouraging since we get very few false positives and a satisfying number of connected components in comparison to other tested algorithms.

Keywords

Data analysis motion detection principal component analysis video sequence analysis video surveillance 

References

  1. 1.
    Toyama, K., Krumm, J., Brummit, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: Proc. IEEE Int. Conf. on Computer Vision (ICCV’99), vol. 1, Kerkyra, Corfu, Greece, Sept. 1999, pp. 255–261. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  2. 2.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104(2), 90–126 (2006)CrossRefGoogle Scholar
  3. 3.
    Tian, Y.-L., Hampapur, A.: Robust salient motion detection with complex background for real-time video surveillance. In: IEEE Workshop on Motion and Video Computing, vol. II, Breckenridge, CO, pp. 30–35. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar
  4. 4.
    Yang, T., Li, S.Z., Pan, Q., Li, J.: Real-time and accurate segmentation of moving objects in dynamic scene. In: Proc. ACM 2nd Int. Workshop on Video Surveillance & Sensor Networks (VSSN 2004), Oct. 2004, pp. 136–143. ACM Press, New York (2004)CrossRefGoogle Scholar
  5. 5.
    McKenna, S.J., Jabri, S., Duric, Z., Wechsler, H., Rosenfeld, A.: Tracking goups of people. Computer Vision and Image Understanding 80(1), 42–56 (2000)zbMATHCrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Koller, D., Weber, J., Malik, J.: Robust multiple car tracking with occlusion reasoning. Technical Report UCB/CSD-93-780, University of California at Berkeley, EECS Department, Berkeley, CA (1993)Google Scholar
  8. 8.
    Ma, Y.-F., Zhang, H.-J.: Detecting motion object by spatio-temporal entropy. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2001), Tokyo, Japan, pp. 265–268. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  9. 9.
    Guo, J., Chng, E.S., Rajan, D.: Foreground motion detection by difference-based spatial temporal entropy image. In: Proc. IEEE Region 10 Conf (TenCon 2004), Chiang Mai, Thailand, pp. 379–382. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  10. 10.
    Fodor, I.K.: A survey of dimension reduction techniques. Report UCRL-ID-148494, Lawrence Livermore National Laboratory, Livermore, CA (2002)Google Scholar
  11. 11.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Nicolas Verbeke
    • 1
  • Nicole Vincent
    • 1
  1. 1.Laboratoire CRIP5-SIP, Université René Descartes Paris 5, 45 rue des Saints-Pères, 75270 Paris Cedex 06France

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