<p align="center">
<img src="https://github.com/andrewssobral/bgslibrary/blob/master/docs/images/bgslibrary-logo.jpg?raw=true" alt="BGSLibrary" width="200">
</p>
# BGSLibrary: A Background Subtraction Library
[](https://github.com/andrewssobral/bgslibrary/wiki/Build-status) [](http://www.gnu.org/licenses/gpl-3.0) [](https://github.com/andrewssobral/bgslibrary/wiki/Build-status) [](https://github.com/andrewssobral/bgslibrary/wiki/Build-status) [](https://github.com/andrewssobral/bgslibrary/wiki/Build-status) [](https://github.com/andrewssobral/bgslibrary/wiki/List-of-available-algorithms) <a href="https://app.commanddash.io/agent?github=https://github.com/andrewssobral/bgslibrary"><img src="https://img.shields.io/badge/AI-Code%20Gen-EB9FDA"></a>
<p align="center">
<a href="https://youtu.be/_UbERwuQ0OU" target="_blank">
<img src="https://raw.githubusercontent.com/andrewssobral/bgslibrary/master/docs/images/bgs_giphy2.gif" border="0" />
</a>
</p>
## Introduction
The **BGSLibrary** (Background Subtraction Library) is a comprehensive C++ framework designed for background subtraction in computer vision applications, particularly for detecting moving objects in video streams. It provides an easy-to-use and extensible platform for researchers and developers to experiment with and implement various background subtraction techniques.
## Library Version
**3.3.0** (see **[Build Status](https://github.com/andrewssobral/bgslibrary/wiki/Build-status)** and **[Release Notes](https://github.com/andrewssobral/bgslibrary/wiki/Release-notes)** for more info)
## Background and Development
The BGSLibrary was developed in early 2012 by [Andrews Cordolino Sobral](http://andrewssobral.wixsite.com/home) as a C++ framework with wrappers available for Python, Java, and MATLAB. It aims to facilitate foreground-background separation in videos using the OpenCV library.
## Compatibility
The library is compatible with OpenCV versions 2.4.x, 3.x, and 4.x. It can be compiled and used on Windows, Linux, and Mac OS X systems.
## Licensing
The library's source code is available under the [MIT license](https://opensource.org/licenses/MIT), making it free for both academic and commercial use.
## Getting started
* [List of available algorithms](https://github.com/andrewssobral/bgslibrary/wiki/List-of-available-algorithms)
* [Algorithms benchmark](https://github.com/andrewssobral/bgslibrary/wiki/Algorithms-benchmark)
* [Which algorithms really matter?](https://github.com/andrewssobral/bgslibrary/wiki/Which-algorithms-really-matter%3F)
* [Library architecture](https://github.com/andrewssobral/bgslibrary/wiki/Library-architecture)
<a href="https://app.commanddash.io/agent?github=https://github.com/andrewssobral/bgslibrary"><img src="https://img.shields.io/badge/AI-Code%20Gen-EB9FDA"></a>
```cpp
#include <iostream>
#include <algorithm>
#include <iterator>
#include <vector>
// Include the OpenCV and BGSLibrary libraries
#include <opencv2/opencv.hpp>
#include <bgslibrary/algorithms/algorithms.h>
int main( int argc, char** argv )
{
// Gets the names of the background subtraction algorithms registered in the BGSLibrary factory
auto algorithmsName = BGS_Factory::Instance()->GetRegisteredAlgorithmsName();
// Displays the number of available background subtraction algorithms in the BGSLibrary
std::cout << "Number of available algorithms: " << algorithmsName.size() << std::endl;
// Displays the list of available background subtraction algorithms in the BGSLibrary
std::cout << "List of available algorithms:" << std::endl;
std::copy(algorithmsName.begin(), algorithmsName.end(), std::ostream_iterator<std::string>(std::cout, "\n"));
// Returns 0 to indicate that the execution was successful
return 0;
}
```
### Installation instructions
You can either install BGSLibrary via [pre-built binary package](https://github.com/andrewssobral/bgslibrary/releases) or build it from source
* [Windows installation](https://github.com/andrewssobral/bgslibrary/wiki/Installation-instructions---Windows)
* [Ubuntu / OS X installation](https://github.com/andrewssobral/bgslibrary/wiki/Installation-instructions-Ubuntu-or-OSX)
Supported Compilers:
* GCC 4.8 and above
* Clang 3.4 and above
* MSVC 2015, 2017, 2019 or newer
Other compilers might work, but are not officially supported.
The bgslibrary requires some features from the ISO C++ 2014 standard.
### Graphical User Interface
* [C++ QT](https://github.com/andrewssobral/bgslibrary/wiki/Graphical-User-Interface:-QT) ***(Official)***
* [C++ MFC](https://github.com/andrewssobral/bgslibrary/wiki/Graphical-User-Interface:-MFC) ***(Deprecated)***
* [Java](https://github.com/andrewssobral/bgslibrary/wiki/Graphical-User-Interface:-Java) ***(Obsolete)***
### Wrappers
* [Python](https://github.com/andrewssobral/bgslibrary/wiki/Wrapper:-Python) [](https://pepy.tech/project/pybgs) [](https://pepy.tech/project/pybgs) [](https://pepy.tech/project/pybgs)
* [MATLAB](https://github.com/andrewssobral/bgslibrary/wiki/Wrapper:-MATLAB)
* [Java](https://github.com/andrewssobral/bgslibrary/wiki/Wrapper:-Java)
### Usage examples
* BGSlibrary examples folder
* * <https://github.com/andrewssobral/bgslibrary/tree/master/examples>
* BGSlibrary examples in C++
* * <https://github.com/andrewssobral/bgslibrary-examples-cpp>
* BGSlibrary examples in Python
* * <https://github.com/andrewssobral/bgslibrary-examples-python>
### More
* [Docker images](https://github.com/andrewssobral/bgslibrary/wiki/Docker-images)
* [How to integrate BGSLibrary in your own CPP code](https://github.com/andrewssobral/bgslibrary/wiki/How-to-integrate-BGSLibrary-in-your-own-CPP-code)
* [How to contribute](https://github.com/andrewssobral/bgslibrary/wiki/How-to-contribute)
* [List of collaborators](https://github.com/andrewssobral/bgslibrary/wiki/List-of-collaborators)
* [Release notes](https://github.com/andrewssobral/bgslibrary/wiki/Release-notes)
## Algorithm compatibility across OpenCV versions
| Algorithm | OpenCV < 3.0 (42) | 3.0 <= OpenCV <= 3.4.7 (41) | 3.4.7 < OpenCV < 4.0 (39) | OpenCV >= 4.0 (26) |
|--------------------------------|:-----------:|:----------------------:|:---------------------:|:------------:|
| AdaptiveBackgroundLearning | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| AdaptiveSelectiveBackgroundLearning | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| CodeBook | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| DPAdaptiveMedian | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| DPEigenbackground | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| DPGrimsonGMM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| DPMean | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| DPPratiMediod | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| DPTexture | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| DPWrenGA | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| DPZivkovicAGMM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| FrameDifference | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| FuzzyChoquetIntegral | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| FuzzySugenoIntegral | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| GMG | :heavy_check_mark: | :x: | :x: | :x: |
| IndependentMultimodal | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| KDE | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| KNN | :x: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| LBAdaptiveSOM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| LBFuzzyAdaptiveSOM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| LBFuzzyGaussian | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| LBMixtureOfGaussians | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| LBP_MRF | :heavy_check_mark: | :heavy_check_mark: | :x: | :x: |
| LBSimpleGaussian | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| LOBSTER | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| MixtureOfGaussianV1 | :heavy_check_mark: | :x: | :x: | :x: |
| MixtureOfGaussianV2 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| MultiCue | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| MultiLayer | :heavy_check_mark: | :heavy_check_mark: | :x: | :x: |
| PAWCS | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| PixelBasedAdaptiveSegmenter | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| SigmaDelta | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| StaticFrameDifference | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| SuBSENSE | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| T2FGMM_UM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| T2FGMM_UV | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| T2FMRF_UM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| T2FMRF_UV | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |
| TwoPoints | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| ViBe | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| VuMeter | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| WeightedMovingMean | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| WeightedMovingVariance | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
## Stargazers over time
[](https://starchart.cc/andrewssobral/bgslibrary)
## Citation
If you use this library for your publications, please cite it as:
```
@inproceedings{bgslibrary,
author = {Sobral, Andrews},
title = {{BGSLibrary}: An OpenCV C++ Background Subtraction Library},
booktitle = {IX Workshop de Visão Computacional (WVC'2013)},
address = {Rio de Janeiro, Brazil},
year = {2013},
month = {Jun},
url = {https://github.com/andrewssobral/bgslibrary}
}
```
A chapter about the BGSLibrary has been published in the handbook on [Background Modeling and Foreground Detection for Video Surveillance](https://sites.google.com/site/backgroundsubtraction/).
```
@incollection{bgslibrarychapter,
author = {Sobral, Andrews and Bouwmans, Thierry},
title = {BGS Library: A Library Framework for Algorithm’s Evaluation in Foreground/Background Segmentation},
booktitle = {Background Modeling and Foreground Detection for Video Surveillance},
publisher = {CRC Press, Taylor and Francis Group.}
year = {2014},
}
```
## References
* Sobral, Andrews. BGSLibrary: An OpenCV C++ Background Subtraction Library. IX Workshop de Visão Computacional (WVC'2013), Rio de Janeiro, Brazil, Jun. 2013. ([PDF](http://www.researchgate.net/publication/257424214_BGSLibrary_An_OpenCV_C_Background_Subtraction_Library) in brazilian-portuguese containing an english abstract).
* Sobral, Andrews; Bouwmans, Thierry. "BGS Library: A Library Framework for Algorithm’s Evaluation in Foreground/Background Segmentation". Chapter on the handbook "Background Modeling and Foreground Detection for Video Surveillance", CRC Press, Taylor and Francis Group, 2014. ([PDF](http://www.researchgate.net/publication/257424214_BGSLibrary_An_OpenCV_C_Background_Subtraction_Library) in english).
Some algorithms of the BGSLibrary were used successfully in the following papers:
* (2014) Sobral, Andrews; Vacavant, Antoine. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Computer Vision and Image Understanding (CVIU), 2014. ([Online](http://dx.doi.org/10.1016/j.cviu.2013.12.005)) ([PDF](http://www.researchgate.net/publication/259340906_A_comprehensive_review_of_background_subtraction_algorithms_evaluated_with_synthetic_and_real_videos))
* (2013) Sobral, Andrews; Oliveira, Luciano; Schnitman, Leizer; Souza, Felippe. (**Best Paper Award**) Highway Traffic Congestion Classification Using Holistic Properties. In International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA'2013), Innsbruck, Austria, Feb 2013. ([Online](http://dx.doi.org/10.2316/P.2013.798-105)) ([PDF](http://www.researchgate.net/publication/233427564_HIGHWAY_TRAFFIC_CONGESTION_CLASSIFICATION_USING_HOLISTIC_PROPERTIES))
## Videos
<p align="center">
<a href="https://www.youtube.com/watch?v=_UbERwuQ0OU" target="_blank">
<img src="https://raw.githubusercontent.com/andrewssobral/bgslibrary/master/docs/images/bgslibrary_qt_gui_video.png" width="600" border="0" />
</a>
</p>
<p align="center">
<a href="https://www.youtube.com/watch?v=Ccqa9KBO9_U" target="_blank">
<img src="https://raw.githubusercontent.com/andrewssobral/bgslibrary/master/docs/images/bgslibrary_youtube.png" width="600" border="0" />
</a>
</p>
Raw data
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"maintainer_email": null,
"keywords": "BGSLibrary, Background Subtraction, Computer Vision, Machine Learning",
"author": "Andrews Cordolino Sobral",
"author_email": "andrewssobral@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/58/22/ac54686f74cd578689b3ab3c6a2723765a67ea95bac39375130d6274fd91/bgslibrary-3.3.0.post2.tar.gz",
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"description": "<p align=\"center\">\n<img src=\"https://github.com/andrewssobral/bgslibrary/blob/master/docs/images/bgslibrary-logo.jpg?raw=true\" alt=\"BGSLibrary\" width=\"200\">\n</p>\n\n# BGSLibrary: A Background Subtraction Library\n\n[](https://github.com/andrewssobral/bgslibrary/wiki/Build-status) [](http://www.gnu.org/licenses/gpl-3.0) [](https://github.com/andrewssobral/bgslibrary/wiki/Build-status) [](https://github.com/andrewssobral/bgslibrary/wiki/Build-status) [](https://github.com/andrewssobral/bgslibrary/wiki/Build-status) [](https://github.com/andrewssobral/bgslibrary/wiki/List-of-available-algorithms) <a href=\"https://app.commanddash.io/agent?github=https://github.com/andrewssobral/bgslibrary\"><img src=\"https://img.shields.io/badge/AI-Code%20Gen-EB9FDA\"></a>\n\n<p align=\"center\">\n<a href=\"https://youtu.be/_UbERwuQ0OU\" target=\"_blank\">\n<img src=\"https://raw.githubusercontent.com/andrewssobral/bgslibrary/master/docs/images/bgs_giphy2.gif\" border=\"0\" />\n</a>\n</p>\n\n## Introduction\n\nThe **BGSLibrary** (Background Subtraction Library) is a comprehensive C++ framework designed for background subtraction in computer vision applications, particularly for detecting moving objects in video streams. It provides an easy-to-use and extensible platform for researchers and developers to experiment with and implement various background subtraction techniques.\n\n## Library Version\n\n**3.3.0** (see **[Build Status](https://github.com/andrewssobral/bgslibrary/wiki/Build-status)** and **[Release Notes](https://github.com/andrewssobral/bgslibrary/wiki/Release-notes)** for more info)\n\n## Background and Development\n\nThe BGSLibrary was developed in early 2012 by [Andrews Cordolino Sobral](http://andrewssobral.wixsite.com/home) as a C++ framework with wrappers available for Python, Java, and MATLAB. It aims to facilitate foreground-background separation in videos using the OpenCV library.\n\n## Compatibility\n\nThe library is compatible with OpenCV versions 2.4.x, 3.x, and 4.x. It can be compiled and used on Windows, Linux, and Mac OS X systems.\n\n## Licensing\n\nThe library's source code is available under the [MIT license](https://opensource.org/licenses/MIT), making it free for both academic and commercial use.\n\n## Getting started\n\n* [List of available algorithms](https://github.com/andrewssobral/bgslibrary/wiki/List-of-available-algorithms)\n* [Algorithms benchmark](https://github.com/andrewssobral/bgslibrary/wiki/Algorithms-benchmark)\n* [Which algorithms really matter?](https://github.com/andrewssobral/bgslibrary/wiki/Which-algorithms-really-matter%3F)\n* [Library architecture](https://github.com/andrewssobral/bgslibrary/wiki/Library-architecture)\n\n<a href=\"https://app.commanddash.io/agent?github=https://github.com/andrewssobral/bgslibrary\"><img src=\"https://img.shields.io/badge/AI-Code%20Gen-EB9FDA\"></a>\n```cpp\n#include <iostream>\n#include <algorithm>\n#include <iterator>\n#include <vector>\n\n// Include the OpenCV and BGSLibrary libraries\n#include <opencv2/opencv.hpp>\n#include <bgslibrary/algorithms/algorithms.h>\n\nint main( int argc, char** argv )\n{\n // Gets the names of the background subtraction algorithms registered in the BGSLibrary factory\n auto algorithmsName = BGS_Factory::Instance()->GetRegisteredAlgorithmsName();\n\n // Displays the number of available background subtraction algorithms in the BGSLibrary\n std::cout << \"Number of available algorithms: \" << algorithmsName.size() << std::endl;\n\n // Displays the list of available background subtraction algorithms in the BGSLibrary\n std::cout << \"List of available algorithms:\" << std::endl;\n std::copy(algorithmsName.begin(), algorithmsName.end(), std::ostream_iterator<std::string>(std::cout, \"\\n\"));\n\n // Returns 0 to indicate that the execution was successful\n return 0;\n}\n```\n\n### Installation instructions\n\nYou can either install BGSLibrary via [pre-built binary package](https://github.com/andrewssobral/bgslibrary/releases) or build it from source\n\n* [Windows installation](https://github.com/andrewssobral/bgslibrary/wiki/Installation-instructions---Windows)\n* [Ubuntu / OS X installation](https://github.com/andrewssobral/bgslibrary/wiki/Installation-instructions-Ubuntu-or-OSX)\n\nSupported Compilers:\n\n* GCC 4.8 and above\n* Clang 3.4 and above\n* MSVC 2015, 2017, 2019 or newer\n\nOther compilers might work, but are not officially supported.\nThe bgslibrary requires some features from the ISO C++ 2014 standard.\n\n### Graphical User Interface\n\n* [C++ QT](https://github.com/andrewssobral/bgslibrary/wiki/Graphical-User-Interface:-QT) ***(Official)***\n* [C++ MFC](https://github.com/andrewssobral/bgslibrary/wiki/Graphical-User-Interface:-MFC) ***(Deprecated)***\n* [Java](https://github.com/andrewssobral/bgslibrary/wiki/Graphical-User-Interface:-Java) ***(Obsolete)***\n\n### Wrappers\n\n* [Python](https://github.com/andrewssobral/bgslibrary/wiki/Wrapper:-Python) [](https://pepy.tech/project/pybgs) [](https://pepy.tech/project/pybgs) [](https://pepy.tech/project/pybgs)\n* [MATLAB](https://github.com/andrewssobral/bgslibrary/wiki/Wrapper:-MATLAB)\n* [Java](https://github.com/andrewssobral/bgslibrary/wiki/Wrapper:-Java)\n\n### Usage examples\n\n* BGSlibrary examples folder\n* * <https://github.com/andrewssobral/bgslibrary/tree/master/examples>\n* BGSlibrary examples in C++\n* * <https://github.com/andrewssobral/bgslibrary-examples-cpp>\n* BGSlibrary examples in Python\n* * <https://github.com/andrewssobral/bgslibrary-examples-python>\n\n### More\n\n* [Docker images](https://github.com/andrewssobral/bgslibrary/wiki/Docker-images)\n* [How to integrate BGSLibrary in your own CPP code](https://github.com/andrewssobral/bgslibrary/wiki/How-to-integrate-BGSLibrary-in-your-own-CPP-code)\n* [How to contribute](https://github.com/andrewssobral/bgslibrary/wiki/How-to-contribute)\n* [List of collaborators](https://github.com/andrewssobral/bgslibrary/wiki/List-of-collaborators)\n* [Release notes](https://github.com/andrewssobral/bgslibrary/wiki/Release-notes)\n\n## Algorithm compatibility across OpenCV versions\n\n| Algorithm | OpenCV < 3.0 (42) | 3.0 <= OpenCV <= 3.4.7 (41) | 3.4.7 < OpenCV < 4.0 (39) | OpenCV >= 4.0 (26) |\n|--------------------------------|:-----------:|:----------------------:|:---------------------:|:------------:|\n| AdaptiveBackgroundLearning | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| AdaptiveSelectiveBackgroundLearning | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| CodeBook | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| DPAdaptiveMedian | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| DPEigenbackground | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| DPGrimsonGMM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| DPMean | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| DPPratiMediod | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| DPTexture | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| DPWrenGA | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| DPZivkovicAGMM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| FrameDifference | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| FuzzyChoquetIntegral | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| FuzzySugenoIntegral | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| GMG | :heavy_check_mark: | :x: | :x: | :x: |\n| IndependentMultimodal | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| KDE | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| KNN | :x: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| LBAdaptiveSOM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| LBFuzzyAdaptiveSOM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| LBFuzzyGaussian | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| LBMixtureOfGaussians | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| LBP_MRF | :heavy_check_mark: | :heavy_check_mark: | :x: | :x: |\n| LBSimpleGaussian | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| LOBSTER | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| MixtureOfGaussianV1 | :heavy_check_mark: | :x: | :x: | :x: |\n| MixtureOfGaussianV2 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| MultiCue | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| MultiLayer | :heavy_check_mark: | :heavy_check_mark: | :x: | :x: |\n| PAWCS | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| PixelBasedAdaptiveSegmenter | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| SigmaDelta | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| StaticFrameDifference | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| SuBSENSE | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| T2FGMM_UM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| T2FGMM_UV | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| T2FMRF_UM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| T2FMRF_UV | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: |\n| TwoPoints | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| ViBe | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| VuMeter | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| WeightedMovingMean | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| WeightedMovingVariance | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n\n## Stargazers over time\n\n[](https://starchart.cc/andrewssobral/bgslibrary)\n\n## Citation\n\nIf you use this library for your publications, please cite it as:\n\n```\n@inproceedings{bgslibrary,\nauthor = {Sobral, Andrews},\ntitle = {{BGSLibrary}: An OpenCV C++ Background Subtraction Library},\nbooktitle = {IX Workshop de Vis\u00e3o Computacional (WVC'2013)},\naddress = {Rio de Janeiro, Brazil},\nyear = {2013},\nmonth = {Jun},\nurl = {https://github.com/andrewssobral/bgslibrary}\n}\n```\n\nA chapter about the BGSLibrary has been published in the handbook on [Background Modeling and Foreground Detection for Video Surveillance](https://sites.google.com/site/backgroundsubtraction/).\n\n```\n@incollection{bgslibrarychapter,\nauthor = {Sobral, Andrews and Bouwmans, Thierry},\ntitle = {BGS Library: A Library Framework for Algorithm\u2019s Evaluation in Foreground/Background Segmentation},\nbooktitle = {Background Modeling and Foreground Detection for Video Surveillance},\npublisher = {CRC Press, Taylor and Francis Group.}\nyear = {2014},\n}\n```\n\n## References\n\n* Sobral, Andrews. BGSLibrary: An OpenCV C++ Background Subtraction Library. IX Workshop de Vis\u00e3o Computacional (WVC'2013), Rio de Janeiro, Brazil, Jun. 2013. ([PDF](http://www.researchgate.net/publication/257424214_BGSLibrary_An_OpenCV_C_Background_Subtraction_Library) in brazilian-portuguese containing an english abstract).\n\n* Sobral, Andrews; Bouwmans, Thierry. \"BGS Library: A Library Framework for Algorithm\u2019s Evaluation in Foreground/Background Segmentation\". Chapter on the handbook \"Background Modeling and Foreground Detection for Video Surveillance\", CRC Press, Taylor and Francis Group, 2014. ([PDF](http://www.researchgate.net/publication/257424214_BGSLibrary_An_OpenCV_C_Background_Subtraction_Library) in english).\n\nSome algorithms of the BGSLibrary were used successfully in the following papers:\n\n* (2014) Sobral, Andrews; Vacavant, Antoine. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Computer Vision and Image Understanding (CVIU), 2014. ([Online](http://dx.doi.org/10.1016/j.cviu.2013.12.005)) ([PDF](http://www.researchgate.net/publication/259340906_A_comprehensive_review_of_background_subtraction_algorithms_evaluated_with_synthetic_and_real_videos))\n\n* (2013) Sobral, Andrews; Oliveira, Luciano; Schnitman, Leizer; Souza, Felippe. (**Best Paper Award**) Highway Traffic Congestion Classification Using Holistic Properties. In International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA'2013), Innsbruck, Austria, Feb 2013. ([Online](http://dx.doi.org/10.2316/P.2013.798-105)) ([PDF](http://www.researchgate.net/publication/233427564_HIGHWAY_TRAFFIC_CONGESTION_CLASSIFICATION_USING_HOLISTIC_PROPERTIES))\n\n## Videos\n\n<p align=\"center\">\n<a href=\"https://www.youtube.com/watch?v=_UbERwuQ0OU\" target=\"_blank\">\n<img src=\"https://raw.githubusercontent.com/andrewssobral/bgslibrary/master/docs/images/bgslibrary_qt_gui_video.png\" width=\"600\" border=\"0\" />\n</a>\n</p>\n\n<p align=\"center\">\n<a href=\"https://www.youtube.com/watch?v=Ccqa9KBO9_U\" target=\"_blank\">\n<img src=\"https://raw.githubusercontent.com/andrewssobral/bgslibrary/master/docs/images/bgslibrary_youtube.png\" width=\"600\" border=\"0\" />\n</a>\n</p>\n",
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