A Benchmark Dataset for Outdoor Foreground/Background Extraction

  • Antoine Vacavant
  • Thierry Chateau
  • Alexis Wilhelm
  • Laurent Lequièvre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7728)


Most of video-surveillance based applications use a foreground extraction algorithm to detect interest objects from videos provided by static cameras. This paper presents a benchmark dataset and evaluation process built from both synthetic and real videos, used in the BMC workshop (Background Models Challenge). This dataset focuses on outdoor situations with weather variations such as wind, sun or rain. Moreover, we propose some evaluation criteria and an associated free software to compute them from several challenging testing videos. The evaluation process has been applied for several state of the art algorithms like gaussian mixture models or codebooks.


Gaussian Mixture Model Benchmark Dataset Static Camera Real Video Shadow 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 2013

Authors and Affiliations

  • Antoine Vacavant
    • 1
    • 2
  • Thierry Chateau
    • 3
  • Alexis Wilhelm
    • 3
  • Laurent Lequièvre
    • 3
  1. 1.ISITClermont Université, Université d’AuvergneClermont-FerrandFrance
  2. 2.CNRS, UMR6284Clermont-FerrandFrance
  3. 3.Pascal InstituteBlaise Pascal University, CNRS, UMR6602Clermont-FerrandFrance

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