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[](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)
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Last page update: **04/03/2023**
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## Introduction
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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.
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## Library Version
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**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)
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## Background and Development
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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)
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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.
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The **BGSLibrary** was developed in early 2012 by [Andrews Sobral](http://andrewssobral.wixsite.com/home) as a C++ framework (with wrappers available for Python, Java and MATLAB) for foreground-background separation in videos using [OpenCV](http://www.opencv.org/). The bgslibrary is compatible with OpenCV versions 2.4.x, 3.x and 4.x, and can be compiled on Windows, Linux, and Mac OS X. It currently contains **43** algorithms and is available free of charge to all users, both academic and commercial. The library's source code is available under the [MIT license](https://opensource.org/licenses/MIT).
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## Compatibility
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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.
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## Licensing
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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.
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## Getting started
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*[List of available algorithms](https://github.com/andrewssobral/bgslibrary/wiki/List-of-available-algorithms)
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/).
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```
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@incollection{bgslibrarychapter,
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author = {Sobral, Andrews and Bouwmans, Thierry},
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}
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```
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## References
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Download PDF:
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* 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).
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* 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).
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Some references
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---------------
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Some algorithms of the BGSLibrary were used successfully in the following papers:
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Some algorithms of the BGSLibrary were used successfully in the following papers:
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* (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))
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* (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))
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