【opencv】动态背景下运动目标检测 SURF配准差分

主要思路是，读入视频，隔帧采用SURF计算匹配的特征点，进而计算两图的投影映射矩阵，做差分二值化，连通域检测，绘制目标。

如果背景是静态的采用camshift即可。

本文方法速度debug下大概2-3帧，release下8-9帧（SURF部分，不包含连通域以及绘制），后续可增加选定目标，动态模版小邻域中跟踪目标。实现对动态背景下的运动目标检测，模版跟踪速度可达150帧。

 

环境：opencv2.4.9 + vs2012

#include 
#include 
#include      

using namespace cv;
using namespace std;

void main()
{
    //VideoCapture capture(0);
    VideoCapture capture("3.mov");

    Mat image01,image02,imgdiff;

    while (true)
    {
        //隔两帧配准
        capture >> image01;
        
        if (image01.empty())
        {
            break;
        }
        
        capture >> image02;
        capture >> image02;
        
        if (image02.empty())
        {
            break;
        }
        //GaussianBlur(image02, image02, Size(3,3), 0);

        double time0 = static_cast(getTickCount());//开始计时
        
        //灰度图转换  
        Mat image1,image2;    
        cvtColor(image01,image1,CV_RGB2GRAY);  
        cvtColor(image02,image2,CV_RGB2GRAY);  

        //提取特征点    
        SurfFeatureDetector surfDetector(2500);  // 海塞矩阵阈值，高一点速度会快些
        vector keyPoint1,keyPoint2;    
        surfDetector.detect(image1,keyPoint1);    
        surfDetector.detect(image2,keyPoint2);    

        //特征点描述，为下边的特征点匹配做准备    
        SurfDescriptorExtractor SurfDescriptor;    
        Mat imageDesc1,imageDesc2;    
        SurfDescriptor.compute(image1,keyPoint1,imageDesc1);    
        SurfDescriptor.compute(image2,keyPoint2,imageDesc2);      

        //获得匹配特征点，并提取最优配对     
        FlannBasedMatcher matcher;  
        vector matchePoints;    
        matcher.match(imageDesc1,imageDesc2,matchePoints,Mat());  
        sort(matchePoints.begin(),matchePoints.end()); //特征点排序    

        //获取排在前N个的最优匹配特征点  
        vector imagePoints1,imagePoints2;      
        for(int i=0; i<(int)(matchePoints.size()*0.25); i++)  
        {         
            imagePoints1.push_back(keyPoint1[matchePoints[i].queryIdx].pt);       
            imagePoints2.push_back(keyPoint2[matchePoints[i].trainIdx].pt);       
        }  

        //获取图像1到图像2的投影映射矩阵 尺寸为3*3  
        Mat homo=findHomography(imagePoints1,imagePoints2,CV_RANSAC);      
        //cout<<"变换矩阵为：\n"<> contours;
        findContours(temp, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);

        if (contours.size()<1)
        {
            continue;
        }

        for (int k = 0; k < contours.size(); k++)
        {
            Rect bomen = boundingRect(contours[k]);

            //省略由于配准带来的边缘无效信息
            if (bomen.x > image02temp.cols * (1 - m_BiLi) && bomen.y > image02temp.rows * (1 - m_BiLi) 
                && bomen.x + bomen.width < image02temp.cols * m_BiLi && bomen.y + bomen.height < image02temp.rows * m_BiLi)
            {
                rectangle(image02temp, bomen, Scalar(255,0,255), 2, 8, 0);
            }

        }
        /*
        for (int i = 50; i < image02.rows - 100; i++)
        {
            for (int j = 50; j < image02.cols - 100; j++)
            {
                uchar pixel = temp.at(i,j);
                if (pixel == 255)
                {
                    Rect bomen(j-7, i-7, 14, 14);
                    rectangle(image02, bomen, Scalar(255,255,255),1,8,0);
                }
            }
        }
        */
        imshow("检测与跟踪",image02temp);

        waitKey(20);    
    }    
}

检测远处运动的车辆

surf消除误匹配点

int surf2(Mat image01, Mat image02)
{
    Mat image1,image2;    
    image1=image01.clone();  
    image2=image02.clone();  
  
    //提取特征点    
    SurfFeatureDetector surfDetector(2000);  //hessianThreshold,海塞矩阵阈值，并不是限定特征点的个数   
    vector keyPoint1,keyPoint2;    
    surfDetector.detect(image1,keyPoint1);    
    surfDetector.detect(image2,keyPoint2);    
  
    //绘制特征点    
    drawKeypoints(image1,keyPoint1,image1,Scalar::all(-1),DrawMatchesFlags::DEFAULT);      
    drawKeypoints(image2,keyPoint2,image2,Scalar::all(-1),DrawMatchesFlags::DRAW_RICH_KEYPOINTS);       
    /*    imshow("KeyPoints of image1",image1);    
    imshow("KeyPoints of image2",image2);   */ 
  
    //特征点描述，为下边的特征点匹配做准备    
    SurfDescriptorExtractor SurfDescriptor;    
    Mat imageDesc1,imageDesc2;    
    SurfDescriptor.compute(image1,keyPoint1,imageDesc1);    
    SurfDescriptor.compute(image2,keyPoint2,imageDesc2);    
  
    //特征点匹配并显示匹配结果    
    //BruteForceMatcher> matcher;    
    FlannBasedMatcher matcher;  
    vector matchePoints;    
    matcher.match(imageDesc1,imageDesc2,matchePoints,Mat());  
  
    //提取强特征点  
    double minMatch=1;  
    double maxMatch=0;  
    for(int i=0;imatchePoints[i].distance?matchePoints[i].distance:minMatch;  
        maxMatch=maxMatch goodMatchePoints;  
   for(int i=0;i(),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);     

    imwrite("匹配图.jpg",imageOutput);
    
    return 0;    
}