Moving object detection using adaptive subband decomposition and fractional lower-order statistics in video sequences

  • Authors:

  • A. Murat Bagci;Yasemin Yardimci;A. Enis Çetin

  • Affiliations:

  • Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey and Electrical and Computer Engineering Department, University of Illinois at Chicago, Chicago, IL;Informatics Institute, Middle East Technical University, Ankara, Turkey;Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey

  • Venue:
  • Signal Processing - Signal processing with heavy-tailed models
  • Year:
  • 2002

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Abstract

In this paper, a moving object detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the "low-high" and "high-low" subimages moving objects appear as outliers and they are detected using a statistical detection test based on fractional lower-order statistics. It turns out that the distribution of the subimage pixels is almost Gaussian in general. On the other hand, at the object boundaries the distribution of the pixels in the subimages deviates from Gaussianity due to the existence of outliers. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented.