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The Background Subtraction Problem for Video Surveillance Systems

  • Alan McIvor
  • Qi Zang
  • Reinhard Klette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1998)

Abstract

This paper reviews papers on tracking people in a video surveillance system, and it presents a newsy stem designed for being able to cope with shadows in a real-time application for counting people which is one of the remaining main problems in adaptive background subtraction in such video surveillance systems.

Keywords

Background Subtraction Background Model Current Frame Background Estimate Current Pixel 
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.

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References

  1. 1.
    Y. Bar-Shalom, Th. E. Fortmann: Tracking and Data Association. Academic Press, NewY ork (1988).zbMATHGoogle Scholar
  2. 2.
    D. Beymer, Ph. McLauchlan, B. Coifman, J. Malik: A real-time computer vision system for measuring traffic parameters. CVPR’97 (1997) 495–501.Google Scholar
  3. 3.
    A. Blake, M. Isard: Active Contours. Springer, Berlin (1998).Google Scholar
  4. 4.
    T. E. Boult, R. Micheals, X. Gao, P. Lewis, C. Power, W. Yin, A. Erkan: Frame-rate omnidirectional surveillance and tracking of camouffaged and occluded targets. Second IEEE Workshop on Visual Surveillance. (1999) 48–55. 177, 178Google Scholar
  5. 5.
    R. Cutler, L. Davis: View-based detection and analysis of periodic motion. Internat. Conf. Pattern Recognition (1998) 495–500. 177Google Scholar
  6. 6.
    A. Elgammal, D. Harwood, L. A. Davis: Non-parametric model for background subtraction. ICCV’99 (1999). 176Google Scholar
  7. 7.
    W. E. L. Grimson, C. Stauffer, R. Romano, L. Lee: Using adaptive tracking to classify and monitor activities in a site. Computer Vision and Pattern Recognition (1998) 1–8. 177Google Scholar
  8. 8.
    W. E. L. Grimson, C. Stauffer: Adaptive background mixture models for real-time tracking. CVPR’99 (1999).Google Scholar
  9. 9.
    I. Haritaoglu, D. Harwood, L. S. Davis: W4: Who? When? Where? What? A real time system for detecting and tracking people. 3rd Face and Gesture Recognition Conf. (1998) 222–227. 177Google Scholar
  10. 10.
    I. Haritaoglu, R. Cutler, D. Harwood, L. S. Davis: Backpack: Detection of people carrying objects using silhouettes. Internat. Conf. Computer Vision (1999) 102–107. 177Google Scholar
  11. 11.
    I. Haritaoglu, D. Harwood, L. S. Davis: Hydra: Multiple people detection and tracking using silhouettes. 2nd IEEE Workshop on Visual Surveillance (1999) 6–13. 177Google Scholar
  12. 12.
    J. Heikkila, O. Silven: A real-time system for monitoring of cyclists and pedestrians. 2nd IEEE Workshop on Visual Surveillance (1999) 74–81. 177, 178, 179Google Scholar
  13. 13.
    T. Horprasert, D. Harwood, L. A. Davis: A statistical approach for real-time robust background subtraction and shadowde tection. ICCV’99 Frame Rate Workshop (1999) 1–19. 176, 179, 180Google Scholar
  14. 14.
    T. Y. Ivanov, C. Stauffer, A. Bobick, W. E. L. Grimson Video Surveillance of Interactions. 2nd IEEE Workshop on Visual Surveillance (1999) 82–90. 177Google Scholar
  15. 15.
    M.-S. Lee: Detecting people in cluttered indoor scenes. CVPR’00 (2000).Google Scholar
  16. 16.
    S. J. McKenna, Y. Raja, S. Gong: Tracking color objects using adaptive mixture models. Image and Vision Computing (1999) 780–785.Google Scholar
  17. 17.
    J. Orwell, P. Remagnino, G. A. Jones: Multi-camera color tracking. 2nd IEEE Workshop on Visual Surveillance (1999) 14–24. 177Google Scholar
  18. 18.
    R. Rosales, S. Sclaro.: 3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions. Computer Vision and Pattern Recognition (1999) 117–123.Google Scholar
  19. 19.
    P. L. Rosin: Thresholding for change detection. ICCV’98 (1998) 274–279.Google Scholar
  20. 20.
    C. Stauffer, W. E. L. Grimson, Adaptive background mixture models for real-time tracking. Computer Vision and Pattern Recognition (1999) 246–252. 177Google Scholar
  21. 21.
    K. Toyama, J. Krumm, B. Brumitt, B. Meyers:Wallflower: Principles and practice of background maintenance. Internat. Conf. Computer Vision (1999) 255–261. 178Google Scholar
  22. 22.
    C. Wren, A. Azabayejani, T. Darrell, A. Pentland: Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Analysis and Machine Intelligence (1997) 780–785.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alan McIvor
    • 2
  • Qi Zang
    • 1
  • Reinhard Klette
    • 1
  1. 1.CITRUniversity of AucklandAucklandNew Zealand
  2. 2.Reveal Ltd.AucklandNew Zealand

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