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An Eigenbackground Subtraction Method Using Recursive Error Compensation

  • Zhifei Xu
  • Pengfei Shi
  • Irene Yu-Hua Gu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4261)

Abstract

Eigenbackground subtraction is a commonly used method for moving object detection. The method uses the difference between an input image and the reconstructed background image for detecting foreground objects based on eigenvalue decomposition. In the method, foreground regions are represented in the reconstructed image using eigenbackground in the sense of least mean squared error minimisation. This results in errors that are spread over the entire reconstructed reference image. This will also result in degradation of quality of reconstructed background leading to inaccurate moving object detection. In order to compensate these regions, an efficient method is proposed by using recursive error compensation and an adaptively computed threshold. Experiments were conducted on a range of image sequences with variety of complexity. Performance were evaluated both qualitatively and quantitatively. Comparisons made with two existing methods have shown better approximations of the background images and more accurate detection of foreground objects have been achieved by the proposed method.

Keywords

Input Image Object Detection Background Image Foreground Object Move Object Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhifei Xu
    • 1
  • Pengfei Shi
    • 1
  • Irene Yu-Hua Gu
    • 2
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiP.R. China
  2. 2.Department of Signals and SystemsChalmers University of TechnologyGothenburgSweden

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