Warping Background Subtraction

Abstract

We present a background model that differentiates between background motion and foreground objects. Unlike most models that represent the variability of pixel intensity at a particular location in the image, we model the underlying warping of pixel locations arising from background motion. The background is modeled as a set of warping layers, where at any given time, different layers may be visible due to the motion of an occluding layer. Foreground regions are thus defined as those that cannot be modeled by some composition of some warping of these background layers. We illustrate this concept by first reducing the possible warps to those where the pixels are restricted to displacements within a spatial neighborhood, and then learning the appropriate size of that spatial neighborhood. Then we show how changes in intensity/color histograms of pixel neighborhoods can be used to discriminate foreground and background regions. We find that this approach compares favorably with the state of the art, while requiring less computation.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2010
Accession Number
ADA556070

Entities

People

  • Deborah Estrin
  • Stefano Soatto
  • Teresa Ko

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Accuracy
  • Climate Change
  • Computational Complexity
  • Computations
  • Computer Vision
  • Data Sets
  • Detection
  • Environment
  • Environmental Monitoring
  • Gaussian Distributions
  • Histograms
  • Intensity
  • Learning
  • Models
  • Monitoring
  • Pattern Recognition
  • Precision

Readers

  • Computer Vision.