Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter

  • Authors:
  • Jing Zhong;Stan Sclaroff

  • Affiliations:
  • -;-

  • Venue:
  • ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
  • Year:
  • 2003

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Abstract

The algorithm presented in this paper aims to segment theforeground objects in video (e.g., people) given time-varying,textured backgrounds. Examples of time-varying backgrounds includewaves on water, clouds moving, trees waving in the wind, automobiletraffic, moving crowds, escalators, etc. We have developed a novelforeground-background segmentation algorithm that explicitlyaccounts for the non-stationary nature and clutter-like appearanceof many dynamic textures. The dynamic texture is modeled byanAutoregressive Moving Average Model (ARMA). A robust Kalman filteralgorithm iteratively estimates the intrinsic appearance of thedynamic texture, as well as the regions of the foreground objects.Preliminary experiments with this method have demonstratedpromising results.