LRSLibrary

Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos.

The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems (for more information, please see here and here). Currently the LRSLibrary offers more than 100 algorithms based on matrix and tensor methods. The LRSLibrary was tested successfully in several MATLAB versions (e.g. R2014, R2015, R2016, R2017, on both x86 and x64 versions). It requires minimum R2014b.

Citation

If you use this library for your publications, please cite it as:

@incollection{lrslibrary2015,
author    = {Sobral, Andrews and Bouwmans, Thierry and Zahzah, El-hadi},
title     = {LRSLibrary: Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos},
booktitle = {Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing},
publisher = {CRC Press, Taylor and Francis Group.}
year      = {2015}
}

Additional reference:

@article{bouwmans2015,
author    = {Bouwmans, Thierry and Sobral, Andrews and Javed, Sajid and Jung, Soon Ki and Zahzah, El-hadi},
title     = {Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: {A} Review for a Comparative Evaluation with a Large-Scale Dataset},
journal   = {CoRR},
volume    = {abs/1511.01245}
year      = {2015},
url       = {http://arxiv.org/abs/1511.01245}
}

Stargazers over time

Install

Just do the following steps:

First, clone the repository:

$ git clone --recursive https://github.com/andrewssobral/lrslibrary.git

Then, open your MATLAB and run the following setup script:

>> lrs_setup

That's all!

GUI

The LRSLibrary provides an easy-to-use graphical user interface (GUI) for background modeling and subtraction in videos. First, run the setup script lrs_setup (or run('C:/lrslibrary/lrs_setup')), then run lrs_gui, and enjoy it!

(Click in the image to see the video) <a href="https://www.youtube.com/watch?v=zziJ7-WnvV8" target="_blank"> <img src="https://raw.githubusercontent.com/andrewssobral/lrslibrary/master/figs/lrslibrary_gui2.png" width="500" border="0" /> </a>

Each algorithm is classified by its cpu time consumption with the following icons:

The algorithms were grouped in eight categories: RPCA for Robust PCA, ST for Subspace Tracking, MC for Matrix Completion, TTD for Three-Term Decomposition, LRR for Low-Rank Representation, NMF for Non-negative Matrix Factorization, NTF for Non-negative Tensor Factorization, or TD for standard Tensor Decomposition.

List of the algorithms available in LRSLibrary

RPCA: Robust PCA (44)
- RPCA: Robust Principal Component Analysis (De la Torre and Black, 2001) website
- PCP: Principal Component Pursuit (Candes et al. 2009)
- FPCP: Fast PCP (Rodriguez and Wohlberg, 2013)
- R2PCP: Riemannian Robust Principal Component Pursuit (Hintermüller and Wu, 2014)
- AS-RPCA: Active Subspace: Towards Scalable Low-Rank Learning (Liu and Yan, 2012)
- ALM: Augmented Lagrange Multiplier (Tang and Nehorai 2011)
- EALM: Exact ALM (Lin et al. 2009) website
- IALM: Inexact ALM (Lin et al. 2009) website
- IALM_LMSVDS: IALM with LMSVDS (Liu et al. 2012)
- IALM_BLWS: IALM with BLWS (Lin and Wei, 2010)
- APG_PARTIAL: Partial Accelerated Proximal Gradient (Lin et al. 2009) website
- APG: Accelerated Proximal Gradient (Lin et al. 2009) website
- DUAL: Dual RPCA (Lin et al. 2009) website
- SVT: Singular Value Thresholding (Cai et al. 2008) website
- ADM: Alternating Direction Method (Yuan and Yang, 2009)
- LSADM: LSADM (Goldfarb et al. 2010)
- L1F: L1 Filtering (Liu et al. 2011)
- DECOLOR: Contiguous Outliers in the Low-Rank Representation (Zhou et al. 2011) website1 website2
- RegL1-ALM: Low-Rank Matrix Approximation under Robust L1-Norm (Zheng et al. 2012) website
- GA: Grassmann Average (Hauberg et al. 2014) website
- GM: Grassmann Median (Hauberg et al. 2014) website
- TGA: Trimmed Grassmann Average (Hauberg et al. 2014) website
- STOC-RPCA: Online Robust PCA via Stochastic Optimization (Feng et al. 2013) website
- MoG-RPCA: Mixture of Gaussians RPCA (Zhao et al. 2014) website
- noncvxRPCA: Robust PCA via Nonconvex Rank Approximation (Kang et al. 2015)
- NSA1: Non-Smooth Augmented Lagrangian v1 (Aybat et al. 2011)
- NSA2: Non-Smooth Augmented Lagrangian v2 (Aybat et al. 2011)
- PSPG: Partially Smooth Proximal Gradient (Aybat et al. 2012)
- flip-SPCP-sum-SPG: Flip-Flop version of Stable PCP-sum solved by Spectral Projected Gradient (Aravkin et al. 2014)
- flip-SPCP-max-QN: Flip-Flop version of Stable PCP-max solved by Quasi-Newton (Aravkin et al. 2014)
- Lag-SPCP-SPG: Lagrangian SPCP solved by Spectral Projected Gradient (Aravkin et al. 2014)
- Lag-SPCP-QN: Lagrangian SPCP solved by Quasi-Newton (Aravkin et al. 2014)
- FW-T: SPCP solved by Frank-Wolfe method (Mu et al. 2014) website
- BRPCA-MD: Bayesian Robust PCA with Markov Dependency (Ding et al. 2011) website
- BRPCA-MD-NSS: BRPCA-MD with Non-Stationary Noise (Ding et al. 2011) website
- VBRPCA: Variational Bayesian RPCA (Babacan et al. 2011)
- PRMF: Probabilistic Robust Matrix Factorization (Wang et al. 2012) website
- OPRMF: Online PRMF (Wang et al. 2012) website
- MBRMF: Markov BRMF (Wang and Yeung, 2013) website
- TF

Lrslibrary

Install / Use

README

LRSLibrary

Citation

Stargazers over time

Install

GUI

List of the algorithms available in LRSLibrary