Background Modeling via RPCA

Recent advances in RPCA are based on decomposition in low-rank and sparse matrices and differ from the decomposition, the related optimization problem and the solvers. These different approaches can be classified as follows:



Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ.  Rochelle, France.

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As this website gives many information that come from my research, please cite my following survey papers:
 
T. Bouwmans, “Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview”, Computer Science Review, 2014. [pdf]

T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]

T. Bouwmans, "Recent Advanced Statistical Background Modeling for Foreground Detection: A Systematic Survey",
Recent Patents on Computer Science, Volume 4, No. 3, pages147-176, September 2011.
[pdf]

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