【OpenCV】高斯混合背景建模

OpenCV中实现了两个版本的高斯混合背景/前景分割方法（Gaussian Mixture-based Background/Foreground Segmentation Algorithm）[1-2]，调用接口很明朗，效果也很好。

BackgroundSubtractorMOG 使用示例

int main(){
	VideoCapture video("1.avi");
	Mat frame,mask,thresholdImage, output;
	video>>frame;
	BackgroundSubtractorMOG bgSubtractor(20,10,0.5,false);
	while(true){
		video>>frame;
		++frameNum;
		bgSubtractor(frame,mask,0.001);
		imshow("mask",mask);
		waitKey(10);
	}
	return 0;
}

构造函数可以使用默认构造函数或带形参的构造函数：

BackgroundSubtractorMOG::BackgroundSubtractorMOG()
BackgroundSubtractorMOG::BackgroundSubtractorMOG(int history, int nmixtures, 
double backgroundRatio, double noiseSigma=0)

其中history为使用历史帧的数目，nmixtures为混合高斯数量，backgroundRatio为背景比例，noiseSigma为噪声权重。

而调用的接口只有重载操作符()：

void BackgroundSubtractorMOG::operator()(InputArray image, OutputArray fgmask, double learningRate=0)

其中image为当前帧图像，fgmask为输出的前景mask，learningRate为背景学习速率。

以下是使用BackgroundSubtractorMOG进行前景/背景检测的一个截图。

BackgroundSubtractorMOG2 使用示例

int main(){
	VideoCapture video("1.avi");
	Mat frame,mask,thresholdImage, output;
	//video>>frame;
	BackgroundSubtractorMOG2 bgSubtractor(20,16,true);
	
	while(true){
		video>>frame;
		++frameNum;
		bgSubtractor(frame,mask,0.001);
		cout<<frameNum<<endl;
		//imshow("mask",mask);
		//waitKey(10);
	}
	return 0;
}

同样的，构造函数可以使用默认构造函数和带形参的构造函数

BackgroundSubtractorMOG2::BackgroundSubtractorMOG2()
BackgroundSubtractorMOG2::BackgroundSubtractorMOG2(int history, 
float varThreshold, bool bShadowDetection=true )

history同上，varThreshold表示马氏平方距离上使用的来判断是否为背景的阈值（此值不影响背景更新速率），bShadowDetection表示是否使用阴影检测（如果开启阴影检测，则mask中使用127表示阴影）。

使用重载操作符()调用每帧检测函数：

void BackgroundSubtractorMOG2::operator()(InputArray image, OutputArray fgmask, double learningRate=-1)

参数意义同BackgroundSubtractorMOG中的operator()函数。

同时BackgroundSubtractorMOG2提供了getBackgroundImage()函数用以返回背景图像：

void BackgroundSubtractorMOG2::getBackgroundImage(OutputArray backgroundImage)

另外OpenCV的refman中说新建对象以后还有其他和模型油有关的参数可以修改，不过比较坑的是opencv把这个这些函数参数声明为protected，同时没有提供访问接口，所以要修改的话还是要自己修改源文件提供访问接口。

protected:
    Size frameSize;
    int frameType;
    Mat bgmodel;
    Mat bgmodelUsedModes;//keep track of number of modes per pixel
    int nframes;
    int history;
    int nmixtures;
    //! here it is the maximum allowed number of mixture components.
    //! Actual number is determined dynamically per pixel
    double varThreshold;
    // threshold on the squared Mahalanobis distance to decide if it is well described
    // by the background model or not. Related to Cthr from the paper.
    // This does not influence the update of the background. A typical value could be 4 sigma
    // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
    /////////////////////////
    // less important parameters - things you might change but be carefull
    ////////////////////////
    float backgroundRatio;
    // corresponds to fTB=1-cf from the paper
    // TB - threshold when the component becomes significant enough to be included into
    // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
    // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
    // it is considered foreground
    // float noiseSigma;
    float varThresholdGen;
    //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
    //when a sample is close to the existing components. If it is not close
    //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
    //Smaller Tg leads to more generated components and higher Tg might make
    //lead to small number of components but they can grow too large
    float fVarInit;
    float fVarMin;
    float fVarMax;
    //initial variance  for the newly generated components.
    //It will will influence the speed of adaptation. A good guess should be made.
    //A simple way is to estimate the typical standard deviation from the images.
    //I used here 10 as a reasonable value
    // min and max can be used to further control the variance
    float fCT;//CT - complexity reduction prior
    //this is related to the number of samples needed to accept that a component
    //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
    //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
    //shadow detection parameters
    bool bShadowDetection;//default 1 - do shadow detection
    unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result - 127 default value
    float fTau;
    // Tau - shadow threshold. The shadow is detected if the pixel is darker
    //version of the background. Tau is a threshold on how much darker the shadow can be.
    //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
    //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.

以下是使用BackgroundSubtractorMOG2检测的前景和背景：

参考文献：

[1] KaewTraKulPong, Pakorn, and Richard Bowden. "An improved adaptive background mixture model for real-time tracking with shadow detection." Video-Based Surveillance Systems. Springer US, 2002. 135-144.
[2] Zivkovic, Zoran. "Improved adaptive Gaussian mixture model for background subtraction." Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. Vol. 2. IEEE, 2004.

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