opencv-视频处理-实时的前景检测-Vibe算法

vibe算法是一种像素级的前景检测算法，实时性高，内存占有率低，前景检测准确率高。但是会出现“鬼影”,当然基于对鬼影的处理，也会有相应的对vibe算法的改进。

把下面三篇文章看明白，基本就会掌握vibe算法的过程：

《 ViBe: a powerful random technique to estimate the background in video sequences》

《Background Subtraction: Experiments and Improvements for ViBe 》

《ViBe: A universal background subtraction algorithm for video sequences》

该算法的原文链接地址，作者已经给出了C代码。

以下用opencv复现一遍。

原理：

论文中以(x,y)为中心,取3x3的区域，即(x,y)处的8-领域。也可以选择5x5区域，即24-领域

对（x,y）处的8-领域，按平均分布随机抽样numberSamples次，论文中给出的是numberSamples=20，假设以下为随机取样的结果：

做为下一帧（x,y）处的背景模型。

问题1：怎么判断视频流中的下一帧坐标(x,y)处是背景点还是前景点？

对于上述的结果，如果“1”的数量大于某一阈值minMatch(文章中设为2)，则视为背景点，并更新背景模型，否则，为前景点。

问题2：更新背景模型的策略

文中给出了伪码，主要基于均匀随机抽样的方法。把背景点，按照一定的概率更新到背景模型中。

代码：（opencv实现）

class OriginalVibe{
public://构造函数OriginalVibe(int _numberSamples, int _minMatch,int _distanceThreshold,int _updateFactor,int _neighborWidth,int _neighborHeight):numberSamples(_numberSamples),minMatch(_minMatch),distanceThreshold(_distanceThreshold),updateFactor(_updateFactor),neighborWidth(_neighborWidth),neighborHeight(_neighborHeight){};   ~OriginalVibe(){};//操作成员变量void setUpdateFactor(int _updateFactor);//灰度图像void originalVibe_Init_GRAY(const Mat &firstFrame);void originalVibe_ClassifyAndUpdate_GRAY(const Mat &frame,OutputArray &_segmentation);//RGB三通道void originalVibe_Init_BGR(const Mat & firstFrame);void originalVibe_ClassifyAndUpdate_BGR(const Mat &frame,OutputArray &_segmentation);private://背景模型const int numberSamples;std::vector<Mat>  backgroundModel;//像素点的分类判断的参数const int minMatch;int distanceThreshold;//背景模型更新概率int updateFactor;//8-领域(3 x 3)const int neighborWidth;const int neighborHeight;//前景和背景分割const static  unsigned char BACK_GROUND;const static  unsigned char FORE_GROUND; //BGR的距离计算int distanceL1(const  Vec3b &src1,const  Vec3b &src2);float distanceL2(const Vec3b &src1,const    Vec3b &src2);
};

#include"originalVibe.h"
#include<iostream>
const unsigned char OriginalVibe::BACK_GROUND = 0;
const unsigned char OriginalVibe::FORE_GROUND = 255;//操作成员变量
void OriginalVibe::setUpdateFactor(int _updateFactor)
{this->updateFactor = _updateFactor;
}
//第一种方法：最原始的vibe灰度通道
void OriginalVibe::originalVibe_Init_GRAY(const Mat &firstFrame)
{int height = firstFrame.rows;int width = firstFrame.cols;//背景模型分配内存backgroundModel.clear();for(int index = 0;index < this->numberSamples;index++){backgroundModel.push_back(Mat::zeros(height,width,CV_8UC1));}//随机数RNG rng;int cshift;int rshift;for(int r = 0;r < height ;r++){for(int c = 0;c < width ; c++){if( c < neighborWidth/2 || c > width - neighborWidth/2 -1|| r < neighborHeight/2 || r > height - neighborHeight/2 -1){/*随机数的生成方式有很多种*//*cshift = randu<int>()%neighborWidth - neighborWidth/2;rshift = randu<int>()%neighborHeight - neighborHeight/2;*/  cshift = rand()%neighborWidth - neighborWidth/2;rshift = rand()%neighborHeight - neighborHeight/2;for(std::vector<Mat>::iterator it = backgroundModel.begin();it != backgroundModel.end();it++){for(;;){/*cshift = rng.uniform(-neighborWidth/2,neighborWidth/2 + 1);rshift = rng.uniform(-neighborHeight/2,neighborHeight/2 +1 );*/        cshift = abs(randu<int>()%neighborWidth) - neighborWidth/2;rshift = abs(randu<int>()%neighborHeight) - neighborHeight/2;if(!(cshift == 0 && rshift==0))break;}    if(c + cshift < 0 || c + cshift >=width)cshift *= -1;if(r + rshift < 0 || r + rshift >= height)rshift *= -1;(*it).at<uchar>(r,c) = firstFrame.at<uchar>(r+rshift,c+cshift);}}else{for(std::vector<Mat>::iterator it = backgroundModel.begin();it != backgroundModel.end();it++){for(;;){/*cshift = rng.uniform(-neighborWidth/2,neighborWidth/2 + 1);rshift = rng.uniform(-neighborHeight/2,neighborHeight/2 +1 );*/cshift = abs(randu<int>()%neighborWidth) - neighborWidth/2;rshift = abs(randu<int>()%neighborHeight) - neighborHeight/2;if(!(cshift == 0 && rshift == 0))break;}(*it).at<uchar>(r,c) = firstFrame.at<uchar>(r+rshift,c+cshift);}}}}
}
void OriginalVibe::originalVibe_ClassifyAndUpdate_GRAY(const Mat &frame,OutputArray &_segmentation)
{int width = frame.cols;int height = frame.rows;int rshift;int cshift;_segmentation.create(frame.size(),CV_8UC1);Mat segmentation = _segmentation.getMat();RNG rng;for(int r = 0; r < height;r++){for(int c = 0;c < width ;c++){int count = 0;unsigned char pixel = frame.at<uchar>(r,c);//让pixel和背景模板中backgroundModel进行比较for(std::vector<Mat>::iterator it = backgroundModel.begin();it != backgroundModel.end();it++){if( abs( int(pixel) - int( (*it).at<uchar>(r,c)) )  < (this->distanceThreshold) ){count++;//循环到一定阶段，判断count的值是否大于 minMatch,更新背景模型if( count >= this->minMatch){int random = rng.uniform(0,this->updateFactor);if(random == 0){int updateIndex = rng.uniform(0,this->numberSamples);backgroundModel[updateIndex].at<uchar>(r,c) = pixel;}random = rng.uniform(0,this->updateFactor);if(random == 0){if(c < neighborWidth/2 || c > width - neighborWidth/2-1 || r < neighborHeight/2 || r > height - neighborHeight/2-1){for(;;){/*cshift = rng.uniform(-neighborWidth/2,neighborWidth/2 + 1);rshift = rng.uniform(-neighborHeight/2,neighborHeight/2 +1 );*/cshift = abs(randu<int>()%neighborWidth) - neighborWidth/2;rshift = abs(randu<int>()%neighborHeight) - neighborHeight/2;if(!(cshift == 0 && rshift ==0))break;}     if(c + cshift < 0 || c + cshift >=width)cshift *= -1;if(r + rshift < 0 || r + rshift >= height)rshift *= -1;int updateIndex = rng.uniform(0,this->numberSamples);backgroundModel[updateIndex].at<uchar>(r+rshift,c+cshift) = pixel;}else{for(;;){/*cshift = rng.uniform(-neighborWidth/2,neighborWidth/2 + 1);rshift = rng.uniform(-neighborHeight/2,neighborHeight/2 +1 );*/cshift = abs(randu<int>()%neighborWidth) - neighborWidth/2;rshift = abs(randu<int>()%neighborHeight) - neighborHeight/2;if(!(cshift == 0 && rshift==0))break;}                              int updateIndex = rng.uniform(0,this->numberSamples);backgroundModel[updateIndex].at<uchar>(r+rshift,c+cshift) = pixel;}                       }segmentation.at<uchar>(r,c) = this ->BACK_GROUND;break;}}}if( count < this->minMatch)  segmentation.at<uchar>(r,c) = this->FORE_GROUND;}}
}//第三种方法：BGR通道
void OriginalVibe::originalVibe_Init_BGR(const Mat & fristFrame)
{int height = fristFrame.rows;int width = fristFrame.cols;//背景模型分配内存backgroundModel.clear();for(int index = 0;index  < this->numberSamples;index++){backgroundModel.push_back( Mat::zeros(height,width,CV_8UC3) );}//随机数RNG rng;int cshift;int rshift;for(int r =0 ; r < height; r++){for(int c = 0;c < width ;c++){if( c < neighborWidth/2 || c > width - neighborWidth/2 -1|| r < neighborHeight/2 || r > height - neighborHeight/2 -1 ){/*初始化背景模型：开始*/for(vector<Mat>::iterator iter = backgroundModel.begin(); iter != backgroundModel.end();iter++){for(;;){cshift = abs(randu<int>()%neighborWidth) - neighborWidth/2;rshift = abs(randu<int>()%neighborHeight) - neighborHeight/2;if(!(cshift == 0 && rshift==0))break;}if(c + cshift < 0 || c + cshift >=width)cshift *= -1;if(r + rshift < 0 || r + rshift >= height)rshift *=-1;(*iter).at<Vec3b>(r,c) = fristFrame.at<Vec3b>(r+rshift,c+cshift);}}/*初始化背景模型：结束*/else{/*******初始化背景模型：开始******/for(vector<Mat>::iterator iter = backgroundModel.begin(); iter != backgroundModel.end();iter++){for(;;){cshift = abs(randu<int>()%neighborWidth) - neighborWidth/2;rshift = abs(randu<int>()%neighborHeight) - neighborHeight/2;if( !(cshift == 0 && rshift==0) )break;}(*iter).at<Vec3b>(r,c) = fristFrame.at<Vec3b>(r+rshift,c+cshift);}/*****初始化背景模型：结束 ******/}   }}
}float OriginalVibe::distanceL2(const Vec3b & src1,const  Vec3b& src2)
{return pow( pow(src1[0]-src2[0],2.0) +pow(src1[1]-src2[1],2.0) + pow(src1[2] - src2[2],2.0),0.5);
}
int OriginalVibe::distanceL1(const Vec3b & src1,const  Vec3b& src2)
{return abs(src1[0]-src2[0])+abs(src1[1] - src2[1])+abs(src1[2]-src2[2]) ;
}void OriginalVibe::originalVibe_ClassifyAndUpdate_BGR(const Mat &frame,OutputArray &_segmentation)
{//*编号1int height = frame.rows;int width = frame.cols;int cshift;int rshift;_segmentation.create(frame.size(),CV_8UC1);Mat segmentation = _segmentation.getMat();RNG rng;for(int r =0 ;r < height; r++){//编号1-1for(int c = 0;c < width ;c++){//编号1-1-1int count = 0;Vec3b pixel = frame.at<Vec3b>(r,c);for( vector<Mat>::iterator iter = backgroundModel.begin() ;iter != backgroundModel.end(); iter++){//编号1-1-1-1////if( distanceL1(pixel,(*iter).at<Vec3b>(r,c)) < 4.5*this->distanceThreshold ){count++;if(count >= this->minMatch){//第一步:更新模型update/**********开始更新模型*************/int random = rng.uniform(0,this->updateFactor);if(random == 0){int updateIndex = rng.uniform(0,this->numberSamples);backgroundModel[updateIndex].at<Vec3b>(r,c) = pixel;}random = rng.uniform(0,this->updateFactor);if(random == 0){/****************************************/if( c < neighborWidth/2 || c > width - neighborWidth/2-1 || r < neighborHeight/2 || r > height - neighborHeight/2-1 ){for(;;){cshift = abs(randu<int>()%neighborWidth) - neighborWidth/2;rshift = abs(randu<int>()%neighborHeight) - neighborHeight/2;if(!(cshift == 0 && rshift==0))break;}if(c + cshift < 0 || c + cshift >=width)cshift*=-1;if(r + rshift < 0 || r + rshift >= height)rshift*=-1;int updateIndex = rng.uniform(0,this->numberSamples);backgroundModel[updateIndex].at<Vec3b>(r+rshift,c+cshift) = pixel;}else{for(;;){cshift = abs(rand()%neighborWidth) - neighborWidth/2;rshift = abs(rand()%neighborHeight) - neighborHeight/2;if(!(cshift == 0 && rshift==0))break;}int updateIndex = rng.uniform(0,this->numberSamples);backgroundModel[updateIndex].at<Vec3b>(r+rshift,c+cshift) = pixel;}/****************************************/}/**********结束更新模型*************///第二步：分类classifysegmentation.at<uchar>(r,c) = this->BACK_GROUND;break;}}}//编号1-1-1-1if(count < this->minMatch)//classifysegmentation.at<uchar>(r,c) = this->FORE_GROUND;}//编号1-1-1}//编号1-1}//*编号1

vibe前景检测算法的结果：（可以和帧差法做一比较）

【结论】

可以看到vibe算法，并没有像帧差法那样，产生大的空洞，但是会有鬼影出现

《Background Subtraction: Experiments and Improvements for ViBe》对上述原始的vibe算法，做了很多改进：

1、把固定的距离阈值，变为自适应的阈值

2、距离的计算方法改为codebook算法中距离计算公式

3、针对闪光点的判断

等等。下面还得接着研究对vide算法的改进

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