Advertisement

Non-parametric Model for Background Subtraction

  • Ahmed Elgammal
  • David Harwood
  • Larry Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1843)

Abstract

Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel non-parametric background model and a background subtraction approach. The model can handle situations where the background of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes. The model estimates the probability of observing pixel intensity values based on a sample of intensity values for each pixel. The model adapts quickly to changes in the scene which enables very sensitive detection of moving targets. We also show how the model can use color information to suppress detection of shadows. The implementation of the model runs in real-time for both gray level and color imagery. Evaluation shows that this approach achieves very sensitive detection with very low false alarm rates.

Key words

visual motion active and real time vision motion detection non-parametric estimation visual surveillance shadow detection 

References

  1. 1.
    C. R. Wern, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of human body,” IEEE Transaction on Pattern Analysis and Machine Intelligence, 1997.Google Scholar
  2. 2.
    K.-P. Karmann and A. von Brandt, “Moving object recognition using and adaptive background memory,” in Time-Varying Image Processing and Moving Object Recognition, Elsevier Science Publishers B.V., 1990.Google Scholar
  3. 3.
    K.-P. Karmann, A. V. Brandt, and R. Gerl, “Moving object segmentation based on adabtive reference images,” in Signal Processing V: Theories and Application, Elsevier Science Publishers B.V., 1990.Google Scholar
  4. 4.
    D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell, “Towards robust automatic traffic scene analyis in real-time,” in ICPR, 1994.Google Scholar
  5. 5.
    N. Friedman and S. Russell, “Image segmentation in video sequences: A probabilistic approach,” in Uncertainty in Artificial Intelligence, 1997.Google Scholar
  6. 6.
    W.E.L. Grimson, C. Stauffer, and R. Romano, “Using adaptive tracking to classify and monitor activities in a site,” in CVPR, 1998.Google Scholar
  7. 7.
    W.E.L. Grimson and C. Stauffer, “Adaptive background mixture models for realtime tracking,” in CVPR, 1999.Google Scholar
  8. 8.
    D. W. Scott, Mulivariate Density Estimation. Wiley-Interscience, 1992.Google Scholar
  9. 9.
    M. D. Levine, Vision in Man and Machine. McGraw-Hill Book Company, 1985.Google Scholar
  10. 10.
    E. L. Hall, Computer Image Processing and Recognition. Academic Press, 1979.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ahmed Elgammal
    • 1
  • David Harwood
    • 1
  • Larry Davis
    • 1
  1. 1.Computer Vision LaboratoryUniversity of MarylandCollege ParkUSA

Personalised recommendations