1) Survey on a category**Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis**
C. Guyon, T. Bouwmans , E. Zahzah, “Robust Principal Component Analysis for Background
Subtraction: Systematic Evaluation and Comparative Analysis”, INTECH, Principal Component Analysis, Book
1, Chapter 12, page 223-238, March 2012.**Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance**
T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Isssue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. T. Bouwmans, E. Zahzah,"Robust Principal Component Analysis via Decomposition into Low-rank and Sparse Matrices: An overview", Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing", CRC Press, Taylor and Francis Group, May 2016. 2) Survey on all the categories **Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset**
Recent research on problem
formulations based on decomposition into low-rank plus sparse matrices
shows a suitable framework to separate
T. Bouwmans,
A. Sobral, S. Javed, S. Jung, E. Zahzah, "Decomposition into Low-rank
plus Additive Matrices for Background/Foreground Separation: A Review
for a Comparative Evaluation with a Large-Scale Dataset", Computer
Science Review, Volume 23, pages 1-71, February 2017. **Robust Principal Component Analysis, Subspace Learning, and Tracking**
This work provides an overview of the entire field of robust subspace learning and tracking. In particular solutions for three problems are discussed in detail: RPCA via low-rank+sparse matrix decomposition (L+S), RST via L+S, and robust subspace recovery (RSR). RSR assumes that an entire data vector is either an outlier or an inlier. The L+S formulation instead assumes that outliers occur on only a few data vector indices and hence are well modeled as sparse corruptions. Finally, experimental results on a
large-scale dataset called CD.net 2014 are provided. N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust Principal Component Analysis, Subspace Learning, and Tracking ”, IEEE Signal Processing Magazine, 2018. `N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust PCA and Robust Subspace Tracking: A Comparative Evaluation”, IEEE Statistical Signal Processing Workshop,` |

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