双背景建模实现 滞留物与失窃物检测

双背景建模即建立两个背景模型，一个更新快的背景模型：1帧更新一次，一个是更新慢的背景模型：30帧（或者N帧）更新一次。更新背景模型我选用平均加权（cvRuningAvg）

 

效果图为演示 检测出杯子被拿走的情况:

因为快背景更新较快，所以杯子被拿走后，快背景中的杯子会马上消失

而慢背景中的杯子驻留的时间较长。将快慢背景相减 就能获取被子。

 

 

 

 

// lostDetection.cpp : 定义控制台应用程序的入口点。
//
# include "stdafx.h"
# include "opencv/highgui.h"
# include "opencv/cv.h"
# include "opencv/cxcore.h"
//#include "ml.h"
# include <iostream >
# ifdef DEBUG
# pragma comment(lib, "opencv_core231d.lib")
# pragma comment(lib, "opencv_features2d231d.lib")
# pragma comment(lib, "opencv_flann231d.lib")
# pragma comment(lib, "opencv_gpu231d.lib")
# pragma comment(lib, "opencv_highgui231d.lib")
# pragma comment(lib, "opencv_imgproc231d.lib")
# pragma comment(lib, "opencv_ml231d.lib")
# else
# pragma comment(lib, "opencv_core231.lib")
# pragma comment(lib, "opencv_features2d231.lib")
# pragma comment(lib, "opencv_flann231.lib")
# pragma comment(lib, "opencv_gpu231.lib")
# pragma comment(lib, "opencv_highgui231.lib")
# pragma comment(lib, "opencv_imgproc231.lib")
# pragma comment(lib, "opencv_ml231.lib")
# endif
using namespace std;
using namespace cv;
# define UPDATEDELAY 30 //慢背景更新间隔
int _tmain( int argc, _TCHAR * argv[])
{
    CvCapture * cap = cvCreateCameraCapture( 0);
     if ( !cap)
    {
        printf( "failed to open device\n");
        getchar();
         return 0 ;
    }
    IplImage * pImg = cvQueryFrame(cap);
     if ( !pImg)
    {
        printf( "failed to query image\n");
        getchar();
         return 0 ;
    }
    
     // 空循环：因为偶的摄像头在刚启动的时候会自动调光。。。。
     for ( int i = 0 ; i < 100 ; i ++)
    {
        pImg = cvQueryFrame(cap);
    }
     int nwidth = pImg - >width;
     int nheight = pImg - >height;
    IplImage *pImgCurrent = cvCreateImage(cvSize(nwidth,nheight), 8, 1); // 当前帧
    IplImage * pImgFastBk = cvCreateImage(cvSize(nwidth,nheight), 8, 1); // 快背景
    IplImage * pImgSlowBk = cvCreateImage(cvSize(nwidth,nheight), 8, 1); //慢背景
    IplImage * pImgFore = cvCreateImage(cvSize(nwidth,nheight), 8, 1); // 前景
    CvMat *pMatCurrent = cvCreateMat(nheight,nwidth,CV_32FC1);
    CvMat *pMatFastBk = cvCreateMat(nheight,nwidth,CV_32FC1);
    CvMat *pMatSlowBk = cvCreateMat(nheight,nwidth,CV_32FC1);
    CvMat *pMatFore = cvCreateMat(nheight,nwidth,CV_32FC1);
    
     // 初始化
    cvCvtColor(pImg,pImgCurrent,CV_BGR2GRAY);
    cvConvert(pImgCurrent,pMatCurrent);
    cvCopy(pMatCurrent,pMatFastBk);
    cvCopy(pMatCurrent,pMatSlowBk);
     int nwaitkey = - 1;
     int ncount = 0;
     while (nwaitkey != 'a')
    {
        pImg = cvQueryFrame(cap);
         if ( !pImg)
        {
            printf( "failed to query image\n");
             break;
        }
        cvCvtColor(pImg,pImgCurrent,CV_BGR2GRAY);
        cvConvert(pImgCurrent,pMatCurrent);
         // 1. 快背景更新(均值加权)
        cvRunningAvg(pMatCurrent,pMatFastBk, 0. 005);
        
        ncount ++;
     //     2. 慢背景更新
         if (ncount >UPDATEDELAY)
        {
            cvRunningAvg(pMatCurrent,pMatSlowBk, 0. 005);
            ncount = 0 ;
        }
         // 3. 两背景相减
        cvAbsDiff(pMatFastBk,pMatSlowBk,pMatFore);
        cvConvert(pMatFore,pImgFore);
        
        cvShowImage( "foreimage",pImgFore);
         // 4. 阈值分割
        cvThreshold(pImgFore,pImgFore, 60, 255,CV_THRESH_BINARY);
        
        cvConvert(pMatFastBk,pImgFastBk);
        cvConvert(pMatSlowBk,pImgSlowBk);
         // 5 . 轮廓查找
        cvShowImage( "fastbk",pImgFastBk);
        cvShowImage( "slowbk",pImgSlowBk);
        cvShowImage( "currentImg",pImgCurrent);
        cvShowImage( "foreImgeThreshold",pImgFore);
        nwaitkey = cvWaitKey( 40);
    }
    cvReleaseImage( &pImgFastBk);
    cvReleaseImage( &pImgSlowBk);
    cvReleaseImage( &pImgFore);
    cvReleaseImage( &pImgCurrent);
    cvReleaseMat( &pMatFastBk);
    cvReleaseMat( &pMatSlowBk);
    cvReleaseMat( &pMatFore);
    cvReleaseMat( &pMatCurrent);
    
    cvWaitKey( - 1);
     return 0;
}

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转载于:https://www.cnblogs.com/xiaomaLV2/archive/2012/08/08/2628131.html