GMM Background Modeling Using Divergence-Based Weight Updating

  • Juan D. Pulgarin-Giraldo
  • Andres Alvarez-Meza
  • David Insuasti-Ceballos
  • Thierry Bouwmans
  • German Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10125)


Background modeling is a core task of video-based surveillance systems used to facilitate the online analysis of real-world scenes. Nowadays, GMM-based background modeling approaches are widely used, and several versions have been proposed to improve the original one proposed by Stauffer and Grimson. Nonetheless, the cost function employed to update the GMM weight parameters has not received major changes and is still set by means of a single binary reference, which mostly leads to noisy foreground masks when the ownership of a pixel to the background model is uncertain. To cope with this issue, we propose a cost function based on Euclidean divergence, providing nonlinear smoothness to the background modeling process. Achieved results over well-known datasets show that the proposed cost function supports the foreground/background discrimination, reducing the number of false positives, especially, in highly dynamical scenarios.


Background modeling GMM Euclidean divergence 



This work was developed in the framework of the research project entitled “Caracterización de cultivos agrícolas mediante estrategias de teledetección y técnicas de procesamiento de imágenes” (36719) under the grants of “Convocatoria conjunta para el fomento de la investigación aplicada y desarrollo tecnológico” 2016, as well as by program “Doctorados Nacionales convocatoria 647 de 2014” funded by COLCIENCIAS and partial Ph.D. financial support from Universidad Autonoma de Occidente.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Juan D. Pulgarin-Giraldo
    • 1
  • Andres Alvarez-Meza
    • 1
  • David Insuasti-Ceballos
    • 1
  • Thierry Bouwmans
    • 2
  • German Castellanos-Dominguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Laboratoire MIA - Université de La RochelleLa RochelleFrance

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