VIBE运动目标检测算法实现

      近来，有不少人咨询我关于VIBE算法的问题，而且对于有些细节问题懵懵懂懂，索要源码类的，考虑这个算法的应用以及很多人对此有比较深的兴趣，遂将其放在博客上供大家学习。该版本的代码是在学校的时候写的，里面也加入了一些其他的后处理内容，尽管还有不足，但是对于加深对VIBE算法的理解肯定有一定帮助。另外，由于最新版本的代码在公司电脑上，不便提供。关于理论方面的请参考下面二篇文章：

1）VIBE-A powerful random technique to estimatie the background in video sequences.

2) VIBE-A universal background subtraction algorithms for video sequences

   VIBE的头文件Vibe.hpp如下：

#pragma once
#include "stdafx.h"
#define  WINSIZE 3

class Vibe
{
public:
	Vibe(void);
	Vibe(IplImage *img);
	void SetMinMatch(int nthreshold){g_MinMatch=nthreshold;}
	void SetRadius(int radius){g_Radius=radius;}
	void SetSampleNum(int num){g_SampleNum=num;}
	void SetThreshold(double t){g_threshold=t;}
	IplImage* GetForeground(){return g_ForeImg;}
	IplImage* GetSegMask(){return g_SegementMask;}
	void Detect(IplImage *img);	
	void ForegroundCombineEdge(); // 结合边缘信息
	void DeleteSmallAreaInForeground(double minArea=20);//删除小面积区域
	// 实现背景更新机制
	void Update();
	// 实现后处理，主要用形态学算子
	void PostProcess();

public:
	~Vibe(void);

private:	
	void ClearLongLifeForeground(int i_lifeLength=200); // 清除场景中存在时间较长的像素，i_lifeLength用于控制允许存在的最长时间
	double AreaDense(IplImage *pFr,int AI,int AJ,int W,int H); //计算(i,j)处邻域大小为W×H的密度
	int GetRandom(int istart,int iend); // 默认istart=0,iend=15
	int GetRandom(int random);
	int GetRandom();// 产生一个随机数
	// 计算两个像素之间的欧式距离
	double CalcPixelDist(CvScalar bkCs,CvScalar curCs);
	// 按照Kim的方法来计算颜色畸变
	double CalcuColorDist(CvScalar bkCs,CvScalar curCs);
	int g_SampleNum;// Sample number for the models，默认为20
	int g_MinMatch; // 当前像素与背景模型匹配的最少个数，默认为2
	int g_Height;
	int g_Width;
	int g_Radius;// 球体的半径，默认为20
	int g_offset; //边界的宽和高
	double g_threshold; // 距离度量的阈值
	unsigned char ***g_Model;// 保存背景模型	
	IplImage *g_ForeImg;// 保存前景图
	IplImage *g_Edge;

	IplConvKernel* element;

	IplImage *g_SegementMask; //分割掩膜
	IplImage *g_UpdateMask; // 更新掩膜
	IplImage *g_Gray;
	int ** LifeLength; // 记录前景点的生命长度,如果前景点的生命长度到达一定的阈值，则将其融入背景中去,且要随机两次。	
};

对应的实现文件如下Vibe.cpp所示：

#include "StdAfx.h"
#include "Vibe.h"

Vibe::Vibe(void)
{
	g_Radius=20;
	g_MinMatch=2;	
	g_SampleNum=20;
	g_offset=(WINSIZE-1)/2;

}

Vibe::Vibe(IplImage *img)
{
	if (!img)
	{
		cout<<" The parameter referenced to NUll Pointer!"<<endl;
		return;
	}
	this->g_Height=img->height;
	this->g_Width=img->width;

	g_Radius=20;
	g_MinMatch=2;	
	g_SampleNum=20;
	g_threshold=50;
	g_offset=(WINSIZE-1)/2;

	g_ForeImg=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
	g_Gray=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
	g_Edge=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
	g_SegementMask=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
	g_UpdateMask=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);

	element=cvCreateStructuringElementEx(3,3,1,1,CV_SHAPE_CROSS,NULL);

	cvCvtColor(img,g_Gray,CV_BGR2GRAY);
	
	// 以上完成相关的初始化操作
	/**********************  以下实现第一帧在每个像素的8邻域内的采样功能，建立对应的背景模型*****************************/
	
	int i=0,j=0,k=0;
	g_Model=new unsigned char**[g_SampleNum];
	for (k=0;k<g_SampleNum;k++)
	{
		g_Model[k]=new unsigned char *[g_Height];
		for(i=0;i<g_Height;i++)
		{
			g_Model[k][i]=new unsigned char [g_Width];
			for (j=0;j<g_Width;j++)
			{
				g_Model[k][i][j]=0;
			}
		}
	}
	
	// 采样进行背景建模	
	double dVal;
	int ri=0,rj=0; //随机采样的值
	for (i=g_offset;i<g_Height-g_offset;i++)
	{
		for (j=g_offset;j<g_Width-g_offset;j++)
		{
			// 周围3*3的邻域内进行采样
			for(k=0;k<g_SampleNum;k++)
			{
				ri=GetRandom(i);
				rj=GetRandom(j);
				dVal=cvGetReal2D(g_Gray,ri,rj);		
				g_Model[k][i][j]=dVal;							
			}
		}
	}

	// 初始化前景点掩膜的生命长度
	LifeLength=new int *[g_Height];
	for (i=0;i<g_Height;i++)
	{
		LifeLength[i]=new int [g_Width];
		for(j=0;j<g_Width;j++)
		{
			LifeLength[i][j]=0;
		}
	}
}


void Vibe::Detect(IplImage *img)
{
	cvZero(g_ForeImg);	
	cvCvtColor(img,g_Gray,CV_BGR2GRAY);
	int i=0,j=0,k=0;
	double dModVal,dCurrVal;
	int tmpCount=0;// 距离比较在阈值内的次数
	double tmpDist=0;	
	int iR1,iR2;//产生随机数
	int Ri,Rj; // 产生邻域内X和Y的随机数

	for (i=0;i<g_Height;i++)
	{		
		for (j=0;j<g_Width;j++)
		{			
			if( i < g_offset || j < g_offset || i> g_Height - g_offset || j> g_Width - g_offset )
			{
				cvSetReal2D(g_ForeImg,i,j,0);
				continue;
			}
			else
			{
				tmpCount=0;
				dCurrVal=cvGetReal2D(g_Gray,i,j);				
				for (k=0;k<g_SampleNum && tmpCount<g_MinMatch  ;k++)
				{					
					dModVal=g_Model[k][i][j];
					//tmpDist=CalcPixelDist(dCurrVal,dModVal);
					//tmpDist=CalcuColorDist(dCurrVal,dModVal);	
					tmpDist=fabs(dModVal-dCurrVal);									
					if (tmpDist<g_Radius)
					{
						tmpCount++;
					}					
				}

				//判断是否匹配上
				if (tmpCount>=g_MinMatch)
				{
					cvSetReal2D(g_ForeImg,i,j,0);
					// 背景模型的更新					
					iR1=GetRandom(0,15);
					if (iR1==0)
					{
						iR2=GetRandom();
						g_Model[iR2][i][j]=dCurrVal;						
					}

					//进一步更新邻域模型
					
					iR1=GetRandom(0,15);
					if (iR1==0)
					{
						Ri=GetRandom(i);
						Rj=GetRandom(j);
						iR2=GetRandom();
						g_Model[iR2][Ri][Rj]=dCurrVal;						
					}						
				}
				else
				{
					cvSetReal2D(g_ForeImg,i,j,255);
				}
			}
		}		
	}		
	
	//ForegroundCombineEdge();
	DeleteSmallAreaInForeground(80);
	ClearLongLifeForeground();
	//PostProcess();
}

double Vibe::AreaDense(IplImage *pFr,int AI,int AJ,int W,int H)
{
	if (AI<=2 || AJ<=2 || AJ>=(g_Width-2) || AI>=(g_Height-2))
	{
		return 0;
	}
	int Num=0,i=0,j=0;
	double dVal=0,dense=0;
	int Total=(2*H+1)*(2*W+1);
	for (i=AI-H;i<=AI+H;i++)
	{
		for (j=AJ-W;j<=AJ+W;j++)
		{
			dVal=cvGetReal2D(pFr,i,j);
			if (dVal>200)
			{
				Num++;
			}
		}
	}
	dense=(double)Num/(double)Total;
	return dense;
}

void Vibe::ForegroundCombineEdge()
{	
	cvZero(g_Edge);
	//cvZero(g_SegementMask);
	//cvCopy(g_ForeImg,g_SegementMask);
	cvCanny(g_Gray,g_Edge,30,200,3);
	int i=0,j=0;
	double dense;
	double dVal;
	for (i=g_offset;i<g_Height-g_offset;i++)
	{
		for (j=g_offset;j<g_Width-g_offset;j++)
		{
			dense=AreaDense(g_ForeImg,i,j,2,2);
			dVal=cvGetReal2D(g_Edge,i,j);
			if (dense>0.2 && dVal>200)
			{
				cvSetReal2D(g_ForeImg,i,j,255);
			}
		}
	}

}


void Vibe::DeleteSmallAreaInForeground(double minArea/* =20 */)
{
	//cvZero(g_SegementMask);
	//cvCopy(g_ForeImg,g_SegementMask);
	int region_count = 0;
	CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
	CvMemStorage*  storage = cvCreateMemStorage();
	cvClearMemStorage(storage);
	cvFindContours( g_ForeImg, storage, &first_seq, sizeof(CvContour), CV_RETR_LIST );
	for( seq = first_seq; seq; seq = seq->h_next )
	{
		CvContour* cnt = (CvContour*)seq;
		if( cnt->rect.width * cnt->rect.height < minArea )
		{
			prev_seq = seq->h_prev;
			if( prev_seq )
			{
				prev_seq->h_next = seq->h_next;
				if( seq->h_next ) seq->h_next->h_prev = prev_seq;
			}
			else
			{
				first_seq = seq->h_next;
				if( seq->h_next ) seq->h_next->h_prev = NULL;
			}
		}
		else
		{			
			region_count++;
		}
	}			 
	cvZero(g_ForeImg);
	cvDrawContours(g_ForeImg, first_seq, CV_RGB(0, 0, 255), CV_RGB(0, 0, 255), 10, -1);	

	/*
	CvContourScanner scanner = cvStartFindContours( g_ForeImg, storage,sizeof(CvContour), CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
	CvSeq *contours=NULL,*c=NULL;
	int poly1Hull0=0;
	int nContours=0;
	double perimScale=100;
	while( (c = cvFindNextContour( scanner )) != 0 ) 
	{
		double len = cvContourPerimeter( c );
		double q = (g_ForeImg->height + g_ForeImg->width)/perimScale; // calculate perimeter len threshold
		if( len < q ) //Get rid of blob if it's perimeter is too small
			cvSubstituteContour( scanner, 0 );
		else //Smooth it's edges if it's large enough
		{
			CvSeq* newC;
			if( poly1Hull0 ) //Polygonal approximation of the segmentation 
				newC = cvApproxPoly( c, sizeof(CvContour), storage, CV_POLY_APPROX_DP, 2, 0 ); 
			else //Convex Hull of the segmentation
				newC = cvConvexHull2( c, storage, CV_CLOCKWISE, 1 );
			cvSubstituteContour( scanner, newC );
			nContours++;
		}
	}
	contours = cvEndFindContours( &scanner );
	// paint the found regions back into the image
	cvZero( g_ForeImg );
	for( c=contours; c != 0; c = c->h_next ) 
		cvDrawContours( g_ForeImg, c, cvScalarAll(255), cvScalarAll(0), -1, CV_FILLED, 8,cvPoint(0,0));
	*/

	cvReleaseMemStorage(&storage);	
}

void Vibe::ClearLongLifeForeground(int i_lifeLength/* =200 */)
{
	int i=0,j=0;
	double dVal=0;
	double dLife=0;
	int iR1,iR2=0;
	double dCurrVal=0;
	for (i=g_offset;i<g_Height-g_offset;i++)
	{
		for (j=g_offset;j<g_Width-g_offset;j++)
		{
			dVal=cvGetReal2D(g_ForeImg,i,j);
			dLife=LifeLength[i][j];
			if (dLife>i_lifeLength)
			{
				LifeLength[i][j]=0;
				dCurrVal=cvGetReal2D(g_Gray,i,j);
				// 更新背景模型
				iR1=GetRandom();
				iR2=GetRandom();
				g_Model[iR1][i][j]=dCurrVal;
				g_Model[iR2][i][j]=dCurrVal;
			}
			else
			{
				LifeLength[i][j]=dLife+1;
			}

		}
	}
}

void Vibe::Update()
{
	cvZero(g_UpdateMask);	

}

void Vibe::PostProcess()
{
	cvZero(g_SegementMask);
	cvMorphologyEx(g_ForeImg,g_SegementMask,NULL,element,CV_MOP_OPEN,1);

}

//算颜色畸变
double Vibe::CalcuColorDist(CvScalar bkCs,CvScalar curCs)
{
	double r,g,b,br,bg,bb;
	r=curCs.val[0];
	g=curCs.val[1];
	b=curCs.val[2];

	br=bkCs.val[0];
	bg=bkCs.val[1];
	bb=bkCs.val[2];

	double curDist=r*r+g*g*b*b; 
	double bkDist=br*br+bg*bg+bb*bb;

	double curBK=r*br+g*bg+b*bb;
	double curbkDist=curBK*curBK;
	double SquareP;
	if (bkDist==0.0)
	{
		SquareP=0;
	}
	else
	{
		SquareP=curbkDist/bkDist;
	}
	double dist=sqrtf(curDist-SquareP);
	return dist;	
}

double Vibe::CalcPixelDist(CvScalar bkCs,CvScalar curCs)
{
	double tmpDist=pow(bkCs.val[0]-curCs.val[0],2)+pow(bkCs.val[1]-curCs.val[1],2)+pow(bkCs.val[2]-curCs.val[2],2);
	return sqrtf(tmpDist);
}

int Vibe::GetRandom()
{
	int val = g_SampleNum * 1.0 * rand() / RAND_MAX;	
	if( val == g_SampleNum )
		return val - 1;
	else
		return val;
}

int Vibe::GetRandom(int random)
{
	int val=random-g_offset+rand()%(2*g_offset);
	if (val<random-g_offset)
	{
		val=random-g_offset;
	}
	if (val>random+g_offset)
	{
		val=random+g_offset;
	}	
	return val;	
}

int Vibe::GetRandom(int istart,int iend)
{
	int val=istart+rand()%(iend-istart);
	return val;
}


Vibe::~Vibe(void)
{
	if (g_ForeImg)
	{
		cvReleaseImage(&g_ForeImg);
	}
	if (g_SegementMask)
	{
		cvReleaseImage(&g_SegementMask);
	}
	if (g_UpdateMask)
	{
		cvReleaseImage(&g_UpdateMask);
	}
	if (g_Gray)
	{
		cvReleaseImage(&g_Gray);
	}

	if (g_Model!=NULL)
	{
		delete[]g_Model;
		g_Model=NULL;
	}
}

最后附上调用的main函数；

int _tmain(int argc, _TCHAR* argv[])
{	
	CvCapture *capture=NULL;
	IplImage* frame=NULL;
	IplImage* pForeImg=NULL;
	IplImage* segImg=NULL;	

	char *file_path="E:\\testVideo\\VTS_01_4.avi";  // m1  test2 锦带河  VTS_01_4_2  head rear  VTS_01_6_2  VTS_01_4
	//const char* file_path="E:\\suntektechvideo\\锦带河.avi";  //test2

	capture=cvCreateFileCapture(file_path);
	if (!capture)
	{
		//cout<<"Read Video File Error!"<<endl;
		return -1;
	}
	frame=cvQueryFrame(capture);
	frame=cvQueryFrame(capture);

	cvNamedWindow("img",1);
	cvNamedWindow("foreN",1);
	//cvNamedWindow("seg",1);

	Vibe* pV=new Vibe(frame);

	while(frame=cvQueryFrame(capture))
	{
		pV->Detect(frame);
		pForeImg=pV->GetForeground();
		//segImg=pV->GetSegMask();
		//frame->origin=1;
		//pForeImg->origin=1;
		cvShowImage("img",frame);
		cvShowImage("foreN",pForeImg);
		//cvShowImage("seg",segImg);
		cvWaitKey(1);
	}

	cvReleaseImage(&frame);
	cvReleaseImage(&pForeImg);
	cvReleaseCapture(&capture);
	return 0;	
}

        代码没做过多的注释，但现有的注释应该对于理解代码足够了。另外，对于计算机视觉里的任何一种算法都不是万能的，VIBE也不例外，只能说VIBE相对其他算法有一定的优势，但是还是有相当的不足，其pixel-wise-based的灰度建模方式解决不了pixel-wise建模算法共有的问题，其他必要辅助信息的融合是必要的。