A benchmark dataset for outdoor foreground/background extraction

Authors:
Antoine Vacavant;Thierry Chateau;Alexis Wilhelm;Laurent Lequièvre
Affiliations:
ISIT, Clermont Université, Université d'Auvergne, Clermont-Ferrand, France,CNRS, UMR6284, Clermont-Ferrand, France;Pascal Institute, Blaise Pascal University, CNRS, UMR6602, Clermont-Ferrand, France;Pascal Institute, Blaise Pascal University, CNRS, UMR6602, Clermont-Ferrand, France;Pascal Institute, Blaise Pascal University, CNRS, UMR6602, Clermont-Ferrand, France
Venue:
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Year:
2012

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Abstract

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.