Multi-modal Background Model Initialization

  • Domenico D. BloisiEmail author
  • Alfonso Grillo
  • Andrea Pennisi
  • Luca Iocchi
  • Claudio Passaretti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)


Background subtraction is a widely used technique for detecting moving objects in image sequences. Very often background subtraction approaches assume the availability of one or more clear frames (i.e., without foreground objects) at the beginning of the image sequence in input. This strong assumption is not always correct, especially when dealing with dynamic background. In this paper, we present the results of an on-line and real-time background initialization method, called IMBS, which generates a reliable initial background model even if no clear frames are available. The accuracy of the proposed approach is calculated on a set of seven publicly available benchmark sequences. Experimental results demonstrate that IMBS generates accurate background models with respect to eight different quality metrics.


Background Subtraction Background Model Foreground Object Move Object Detection Background Subtraction Method 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Domenico D. Bloisi
    • 1
    Email author
  • Alfonso Grillo
    • 2
  • Andrea Pennisi
    • 1
  • Luca Iocchi
    • 1
  • Claudio Passaretti
    • 2
  1. 1.Sapienza University of RomeRomeItaly
  2. 2.WT ItaliaRomeItaly

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