Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset

@article{Bouwmans2015DecompositionIL,
  title={Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset},
  author={Thierry Bouwmans and Andrews Sobral and Sajid Javed and Soon Ki Jung and El-hadi Zahzah},
  journal={Comput. Sci. Rev.},
  year={2015},
  volume={23},
  pages={1-71}
}
View PDF on arXiv
301 Citations
Highly Influential Citations
13
Background Citations
98
Methods Citations
65
Results Citations
3

301 Citations

Robust PCA Using Matrix Factorization for Background/Foreground Separation

A fast background/foreground separation algorithm in which the low-rank constraint is solved by a matrix factorization scheme, thus heavily reducing the computation cost and improving the robustness and flexibility of the traditional nuclear norm.

Generalized nuclear norm and Laplacian scale mixture based low-rank and sparse decomposition for video foreground-background separation

Robust Tensor Decomposition Based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

A smooth sparse Robust Tensor Decomposition by decomposing the tensor data into low-rank, smooth, and sparse components, respectively is put forward, which is a highly effective method for background/foreground separation in noisy cases.

Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee

A smooth robust tensor completion (SRTC) model is proposed to recover the data and decompose it into the static background and smooth foreground, respectively, and extensive experiments demonstrate that the proposed method outperforms the state-of-the-art approaches for background/foreground separation with missing pixels.

Background Subtraction via Fast Robust Matrix Completion

The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.

Subtraction via Fast Robust Matrix Completion

The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.

A Batch-Incremental Video Background Estimation Model Using Weighted Low-Rank Approximation of Matrices

This work builds a batch-incremental background estimation model by using a special weighted low-rank approximation of matrices that is superior to the existing state-of-the-art background estimation algorithms such as GRASTA, ReProCS, incPCP, and GFL.

Foreground-Background Separation via Generalized Nuclear Norm and Structured Sparse Norm Based Low-Rank and Sparse Decomposition

This paper introduces the generalized nuclear norm and structured sparse norm (GNNSSN) method based LRSD for video foreground-background separation, and extends the proposed model to a robust model against noise for practical applications.

Robust Tensor PCA based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

A smooth sparse RTPCA (SS-RTPCA) model is proposed to decompose the data into static background, smooth foreground, and noise, respectively, which significantly outperforms the state-of-the-art approaches for background/foreground separation in noisy cases.

Total Variation and Rank-1 Constraint RPCA for Background Subtraction

This work proposes a novel BS method employing the more refined prior representations for the static and dynamic components of the video sequences using the framework of schemes-based robust principal component analysis (RPCA).
...

References

SHOWING 1-10 OF 474 REFERENCES

Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance

Stochastic decomposition into low rank and sparse tensor for robust background subtraction

This work applies the idea of stochastic optimization on tensor for robust low-rank and sparse error separation and reduces the memory and computational complexities of this iterative multi-dimensional tensor data optimization scheme for decomposition.

Background Recovery by Fixed-Rank Robust Principal Component Analysis

Comprehensive tests show that, by fixing the rank of the low-rank matrix to a known value, the fixed-rank algorithm produces more reliable and accurate results than existing low- rank RPCA algorithm.

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing

This handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

Double Nuclear Norm-Based Matrix Decomposition for Occluded Image Recovery and Background Modeling

This paper presents a method called double nuclear norm-based matrix decomposition (DNMD) for dealing with the image data corrupted by continuous occlusion, which uses a unified low-rank assumption to characterize the real image data and continuous Occlusion.

Foreground detection based on low-rank and block-sparse matrix decomposition

This paper proposes to use a RPCA method based on low-rank and block-sparse matrix decomposition to achieve foreground detection and Experimental results on different datasets show the pertinence of the proposed approach.

SBMI-LTD: stationary background model initialization based on low-rank tensor decomposition

Since, maximum norm regularizer is more robust than nuclear norm against large number of outliers, therefore the proposed extended tensor based decomposition framework with maximum norm provides an accurate estimation of background scene.

A fast tri-factorization method for low-rank matrix recovery and completion

Foreground detection via robust low rank matrix factorization including spatial constraint with Iterative reweighted regression

This paper proposes to use a low-rank matrix factorization with IRLS scheme (Iteratively reweighted least squares) and to address in the minimization process the spatial connexity of the pixels and results show the pertinence of the proposed approach.

Robust principal component analysis via capped norms

This paper presents a novel non-convex formulation for the RPCA problem using the capped trace norm and the capped l1-norm, and presents two algorithms to solve the non-Convex optimization: one is based on the Difference of Convex functions (DC) framework and the other attempts to solved the sub-problems via a greedy approach.
...