【opencv】动态背景下运动目标检测 SURF配准差分

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【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 <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/nonfree/nonfree.hpp>     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<double>(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<KeyPoint> 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<DMatch> 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<Point2f> 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"<<homo<<endl<<endl; //输出映射矩阵  //图像配准  Mat imageTransform1,imgpeizhun,imgerzhi;  warpPerspective(image01,imageTransform1,homo,Size(image02.cols,image02.rows));    //imshow("经过透视矩阵变换后",imageTransform1);  absdiff(image02, imageTransform1, imgpeizhun);//imshow("配准diff", imgpeizhun);  threshold(imgpeizhun, imgerzhi, 50, 255.0 , CV_THRESH_BINARY);//imshow("配准二值化", imgerzhi);//输出所需时间time0 = ((double)getTickCount()-time0)/getTickFrequency();cout<<1/time0<<endl;Mat temp,image02temp;float m_BiLi = 0.9;image02temp = image02.clone();cvtColor(imgerzhi,temp,CV_RGB2GRAY);  //检索连通域Mat se=getStructuringElement(MORPH_RECT, Size(5,5));morphologyEx(temp, temp, MORPH_DILATE, se);vector<vector<Point>> 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<uchar>(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<KeyPoint> 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<L2<float>> matcher;    FlannBasedMatcher matcher;  vector<DMatch> matchePoints;    matcher.match(imageDesc1,imageDesc2,matchePoints,Mat());  //提取强特征点  double minMatch=1;  double maxMatch=0;  for(int i=0;i<matchePoints.size();i++)  {  //匹配值最大最小值获取  minMatch=minMatch>matchePoints[i].distance?matchePoints[i].distance:minMatch;  maxMatch=maxMatch<matchePoints[i].distance?matchePoints[i].distance:maxMatch;  }  //最大最小值输出  cout<<"最佳匹配值是： "<<minMatch<<endl;  cout<<"最差匹配值是： "<<maxMatch<<endl;  //获取排在前边的几个最优匹配结果  vector<DMatch> goodMatchePoints;  for(int i=0;i<matchePoints.size();i++)  {  if(matchePoints[i].distance<minMatch+(maxMatch-minMatch)/2)  {  goodMatchePoints.push_back(matchePoints[i]);  }  }  //绘制最优匹配点  Mat imageOutput;  drawMatches(image01,keyPoint1,image02,keyPoint2,goodMatchePoints,imageOutput,Scalar::all(-1),  Scalar::all(-1),vector<char>(),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);     imwrite("匹配图.jpg",imageOutput);return 0;    
}

本文标签： opencv动态背景下运动目标检测 SURF配准差分