bgslibrary：实现了二十来种视频前景提取的算法
原文链接：http://www.sigvc.org/bbs/forum.php?mod=viewthread&tid=993&fromuid=12561
| bgslibrary：A Background Subtraction Library，实现了二十来种视频前景提取的算法。不一定每个都最优，但是可以做一些简单的对比。 https://code.google.com/p/bgslibrary/ The BGSLibrary was developed by Andrews Sobral and provides a C++ framework to perform background subtraction (BGS). The code works either on Windows or on Linux. Currently the library offers 31 BGS algorithms. A large amount of algorithms were provided by several authors. The source code is available under GNU GPL v3 license, the library is free and open source. Any user can be download latest project source code using SVN client. In Windows, a demo project for Visual Studio 2010 is provided. An executable version of BGSLibrary is available for Windows 32 bits and 64 bits. For Linux users, a Makefile can be used to compile all files and generate an executable example. Table 1 lists some of algorithms available in BGSLibrary. The algorithms are classified by their similarities.
Table 1. Algorithms available in BGSLibrary
Table 1.Algorithms available in BGSLibrary
Basic methods, mean and variance overtime:
(StaticFrameDifferenceBGS)
Static FrameDifference
(FrameDifferenceBGS)
FrameDifference
(WeightedMovingMeanBGS)
WeightedMoving Mean
(WeightedMovingVarianceBGS)
WeightedMoving Variance
(AdaptiveBackgroundLearning)
AdaptiveBackground Learning
1 (DPMeanBGS)
Temporal Mean
1 (DPAdaptiveMedianBGS)
AdaptiveMedian
of McFarlaneand Schofield (1995)
paper link
1 (DPPratiMediodBGS)
TemporalMedian
of Cucchiaraet al (2003) and Calderara et al (2006)
paper link1 paperlink2 paper link3
Fuzzy based methods:
2 (FuzzySugenoIntegral)
Fuzzy SugenoIntegral
(with Adaptive-SelectiveUpdate) of Hongxun Zhang and De Xu (2006)
paper link
2 (FuzzyChoquetIntegral)
Fuzzy ChoquetIntegral
(withAdaptive-Selective Update) of Baf et al (2008)
paper link
3 (LBFuzzyGaussian)
FuzzyGaussian
of Sigari etal (2008)
paper link
Statistical methods using one gaussian:
1 (DPWrenGABGS)
GaussianAverage
of Wren(1997)
paper link
3 (LBSimpleGaussian)
SimpleGaussian
of Benezethet al (2008)
paper link
Statistical methods using multiplegaussians:
1 (DPGrimsonGMMBGS)
GaussianMixture Model
of Staufferand Grimson (1999)
paper link
0 (MixtureOfGaussianV1BGS)
GaussianMixture Model
ofKadewTraKuPong and Bowden (2001)
paper link
0 (MixtureOfGaussianV2BGS)
GaussianMixture Model
of Zivkovic(2004)
paper link1 paper link2
1 (DPZivkovicAGMMBGS)
GaussianMixture Model
of Zivkovic(2004)
paper link1 paper link2
3 (LBMixtureOfGaussians)
GaussianMixture Model
of Baf et al(2008)
paper link
Type-2 Fuzzy based methods:
2 (T2FGMM_UM)
Type-2 FuzzyGMM-UM
of Baf et al(2008)
paper link
2 (T2FGMM_UV)
Type-2 FuzzyGMM-UV
of Baf et al(2008)
paper link
2 (T2FMRF_UM)
Type-2 FuzzyGMM-UM with MRF
of Zhao et al(2012)
paper link1 paper link2
2 (T2FMRF_UV)
Type-2 FuzzyGMM-UV with MRF
of Zhao et al(2012)
paper link1 paper link2
Statistical methods using color andtexture features:
1 (DPTextureBGS)
Texture BGS
of Heikkila et al.(2006)
paper link
4 (MultiLayerBGS)
Multi-LayerBGS
of Jian Yaoand Jean-Marc Odobez (2007)
paper link
Non-parametric methods:
5 (PixelBasedAdaptiveSegmenter)
Pixel-BasedAdaptive Segmenter (PBAS)
of Hofmann etal (2012)
paperlink
0 (GMG)
GMG
of Godbehere et al(2012)
paper link
6 (VuMeter)
VuMeter
of Goyat et al (2006)
paper link
7 (KDE)
KDE
of Elgammal et al (2000)
paper link
Methods based on eigen features:
1 (DPEigenbackgroundBGS)
Eigenbackground/ SL-PCA
of Oliver etal (2000)
paper link
Neural and neuro-fuzzy methods:
3 (LBAdaptiveSOM)
Adaptive SOM
of Maddalena andPetrosino (2008)
paper link
3 (LBFuzzyAdaptiveSOM)
FuzzyAdaptive SOM
of Maddalenaand Petrosino (2010)
paper link
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