主要思路是，讀入視頻，隔幀采用SURF計算匹配的特征點，進而計算兩圖的投影映射矩陣，做差分二值化，連通域檢測，繪制目標。

如果背景是靜態的采用camshift即可。

本文方法速度debug下大概2-3幀，release下8-9幀（SURF部分，不包含連通域以及繪制），后續可增加選定目標，動態模版小鄰域中跟蹤目標。實現對動態背景下的運動目標檢測，模版跟蹤速度可達150幀。

環境：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)    {        //隔兩幀配準        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());//開始計時                //灰度圖轉換          Mat image1,image2;            cvtColor(image01,image1,CV_RGB2GRAY);          cvtColor(image02,image2,CV_RGB2GRAY);          //提取特征點            SurfFeatureDetector surfDetector(2500);  // 海塞矩陣閾值，高一點速度會快些        vector<KeyPoint> keyPoint1,keyPoint2;            surfDetector.detect(image1,keyPoint1);            surfDetector.detect(image2,keyPoint2);            //特征點描述，為下邊的特征點匹配做準備            SurfDescriptorExtractor SurfDescriptor;            Mat imageDesc1,imageDesc2;            SurfDescriptor.compute(image1,keyPoint1,imageDesc1);            SurfDescriptor.compute(image2,keyPoint2,imageDesc2);              //獲得匹配特征點，并提取最優配對             FlannBasedMatcher matcher;          vector<DMatch> matchePoints;            matcher.match(imageDesc1,imageDesc2,matchePoints,Mat());          sort(matchePoints.begin(),matchePoints.end()); //特征點排序            //獲取排在前N個的最優匹配特征點          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);               }          //獲取圖像1到圖像2的投影映射矩陣 尺寸為3*3          Mat homo=findHomography(imagePoints1,imagePoints2,CV_RANSAC);              //cout<<"變換矩陣為：/n"<<homo<<endl<<endl; //輸出映射矩陣          //圖像配準          Mat imageTransform1,imgpeizhun,imgerzhi;          warpPerspective(image01,imageTransform1,homo,Size(image02.cols,image02.rows));            //imshow("經過透視矩陣變換后",imageTransform1);          absdiff(image02, imageTransform1, imgpeizhun);        //imshow("配準diff", imgpeizhun);          threshold(imgpeizhun, imgerzhi, 50, 255.0 , CV_THRESH_BINARY);        //imshow("配準二值化", imgerzhi);        //輸出所需時間        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);          //檢索連通域        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]);            //省略由于配準帶來的邊緣無效信息            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("檢測與跟蹤",image02temp);        waitKey(20);        }    }
檢測遠處運動的車輛