bgslibrary 3.3.0.post2

Official Python wrapper for BGSLibrary

BGSLibrary: A Background Subtraction Library

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 and Release Notes for more info)

Background and Development

The BGSLibrary was developed in early 2012 by Andrews Cordolino Sobral 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, making it free for both academic and commercial use.

Getting started

• List of available algorithms
• Algorithms benchmark
• Which algorithms really matter?
• Library architecture

#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 or build it from source

• Windows installation
• Ubuntu / OS X installation

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 (Official)
• C++ MFC (Deprecated)
• Java (Obsolete)

Wrappers

• Python
• MATLAB
• 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
• How to integrate BGSLibrary in your own CPP code
• How to contribute
• List of collaborators
• 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

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},
}

References

• 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 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)

Videos