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}
}
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290 Citations
Highly Influential Citations
11
Background Citations
91
Methods Citations
61
Results Citations
3

290 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.

Robust Low-Rank and Sparse Decomposition for Moving Object Detection: from Matrices to Tensors. (Détection d'objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse: de matrices à tenseurs)

A double-constrained version of robust principal component analysis is proposed to improve the foreground detection in maritime environments for automated video-surveillance applications and the problem of background model initialization as a reconstruction process from missing/corrupted data is addressed.

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 (SS-RTD) 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.

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.
...

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