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:
RPCA via Principal Component Pursuit (490 papers)
RPCA via Outlier Pursuit (3 papers)
RPCA via Sparsity Control (3 papers)
RPCA via Sparse Corruptions (1 paper)
RPCA via Log-sum Heuristic Recovery (3 papers)
RPCA via Stochastic Approximation (14 papers)
Bayesian RPCA (12 papers)
Approximated RPCA (7 papers)
Sparse Additive Matrix Factorization (2 papers)
Variational Bayesian Sparse Estimator (2 papers).
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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