Illumination Invariant Background Model Using Mixture of Gaussians and SURF Features

  • Munir Shah
  • Jeremiah Deng
  • Brendon Woodford
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7728)

Abstract

The Mixture of Gaussians (MoG) is a frequently used method for foreground-background separation. In this paper, we propose an on-line learning framework that allows the MoG algorithm to quickly adapt its localized parameters. Our main contributions are: local parameter adaptations, a feedback based updating method for stopped objects, and hierarchical SURF features matching based ghosts and local illumination suppression method. The proposed model is rigorously tested and compared with several previous models on BMC data set and has shown significant performance improvements.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Munir Shah
    • 1
  • Jeremiah Deng
    • 1
  • Brendon Woodford
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

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