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Non-parametric Model for Background Subtraction

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

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© 2000 Springer-Verlag Berlin Heidelberg

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Elgammal, A., Harwood, D., Davis, L. (2000). Non-parametric Model for Background Subtraction. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45053-X_48

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  • DOI: https://doi.org/10.1007/3-540-45053-X_48

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67686-7

  • Online ISBN: 978-3-540-45053-5

  • eBook Packages: Springer Book Archive