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Recently Saved by saunier on October 12, 2012
First saved by cwr on July 03, 2012
Many real-time vision-based applications devoted to video surveillance search for distinguishing moving objects from an image sequence given by a static camera. This background/foreground segmentation stage could be addressed by simple approaches, e.g. by computing the difference between two successive frames, or by building a time-averaged background image. However, such simple algorithms are very limited in outdoor environment for instance (because of global variation of luminance, shadows of objects,etc.).
Since this is an important step in video analysis, many background subtraction algorithms have been proposed since 90’s to tackle these problems. Although the evaluation of BSA is an important issue, relevant papers that handle with both benchmarks and annotated dataset are limited [2,3]. Moreover, many authors that propose a novel approach use  as a gold-standard, but rarely compare their method with recent related work. BMC is thus an opportunity for researchers at both universities and companies to evaluate the quality of their work.
 C. Stauffer and W. E. L. Grimson. Adaptative background mixture models for a real-time tracking. In International Conference on Computer Vision and Pattern Recognition, 1999.
 Y. Benezeth, P-M. Jodoin, B. Emile, H. Laurent, and C. Rosenberger. Review and evaluation of commonly-implemented background subtraction algorithms. In International Conference on Pattern Recognition, 2008.
 A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara. Detecting moving shadows: Algorithms and evaluation. In IEEE Trans. PAMI, 2003.