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Background Modeling via Incremental Maximum Margin Criterion

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Computer Vision – ACCV 2010 Workshops (ACCV 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6469))

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Abstract

Subspace learning methods are widely used in background modeling to tackle illumination changes. Their main advantage is that it doesn’t need to label data during the training and running phase. Recently, White et al. [1] have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervised subspace learning called Incremental Maximum Margin Criterion (IMMC). The proposed scheme enables to initialize robustly the background and to update incrementally the eigenvectors and eigenvalues. Experimental results made on the Wallflower datasets show the pertinence of the proposed approach.

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Author information

Authors and Affiliations

  1. Laboratoire MIA, University of La Rochelle, 17000, La Rochelle, France

    Cristina Marghes & Thierry Bouwman

Authors
  1. Cristina Marghes
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  2. Thierry Bouwman
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Editor information

Editors and Affiliations

  1. Computer Science Institute, Christian-Albrechts-University Kiel, Germany, Olshausenstr. 40, 24098, Kiel, Germany

    Reinhard Koch

  2. Institute of Computer Science and Information Engineering, National Ilan University, Shen-Lung Rd. 1, 26047, Yi-Lan, Taiwan R.O.C.

    Fay Huang

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© 2011 Springer-Verlag Berlin Heidelberg

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Marghes, C., Bouwman, T. (2011). Background Modeling via Incremental Maximum Margin Criterion. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22819-3_40

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  • DOI: https://doi.org/10.1007/978-3-642-22819-3_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22818-6

  • Online ISBN: 978-3-642-22819-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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