Robust background subtraction with foreground validation for urban traffic video

  • Authors:

  • Sen-Ching S. Cheung;Chandrika Kamath

  • Affiliations:

  • Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA and Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2005

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Abstract

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.