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  • Research Article
  • Open Access

Robust Background Subtraction with Foreground Validation for Urban Traffic Video

EURASIP Journal on Advances in Signal Processing20052005:726261

  • Received: 15 January 2004
  • Published:


Identifying moving objects in a video sequence is a fundamental and critical task in many computer-vision applications. Background subtraction techniques are commonly used to separate foreground moving objects from the background. Most background subtraction techniques assume a single rate of adaptation, which is inadequate for complex scenes such as a traffic intersection where objects are moving at different and varying speeds. In this paper, we propose a foreground validation algorithm that first builds a foreground mask using a slow-adapting Kalman filter, and then validates individual foreground pixels by a simple moving object model built using both the foreground and background statistics as well as the frame difference. Ground-truth experiments with urban traffic sequences show that our proposed algorithm significantly improves upon results using only Kalman filter or frame-differencing, and outperforms other techniques based on mixture of Gaussians, median filter, and approximated median filter.

Keywords and phrases

  • background subtraction
  • foreground validation
  • urban traffic video

Authors’ Affiliations

Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, USA
Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506-0503, USA


© Cheung and Kamath 2005