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Official Python wrapper for BGSLibrary

Project description

BGSLibrary

A Background Subtraction Library

Release License: GPL v3 Platform: Windows, Linux, OS X OpenCV Wrapper: Python, MATLAB Algorithms

Last page update: 04/03/2023

Library Version: 3.3.0 (see Build Status and Release Notes for more info)

The BGSLibrary was developed in early 2012 by Andrews Sobral as a C++ framework (with wrappers available for Python, Java and MATLAB) for foreground-background separation in videos using OpenCV. 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.

You can either install BGSLibrary via pre-built binary package or build it from source

Supported Compilers are:

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.

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

Stargazers over time

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.

@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},
}

Download PDF:

  • Sobral, Andrews. BGSLibrary: An OpenCV C++ Background Subtraction Library. IX Workshop de Visão Computacional (WVC'2013), Rio de Janeiro, Brazil, Jun. 2013. (PDF 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 in english).

Some references

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) (PDF)

  • (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) (PDF)

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