Question

跟踪对象时，我希望能够在遮挡后重新检测到它。

在OpenCV 3.4.5（C ++）上，我尝试了模板匹配和光流分割。但是现在，我想使用HOG描述符实现更强大的算法。

我举了一个小例子来说明问题。这是我的2张图片：

我要检测的车辆

我要搜索的图像

PS：我不想训练SVM，因为我只想在几帧中检测到一个唯一的对象。

我的代码：

#include <opencv2/core/utility.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/objdetect.hpp>

#include <iostream>
#include <vector>

using namespace std;
using namespace cv;

int main(int argc, char** argv){

        //load images
        Mat lastSeenObject=imread("lastSeenObject.png",1); //21x39
        Mat patch=imread("patch.png",1); //150x150

        //params
        Size cellSize(8,8);
        int nbins= 9;
        Size blockSize(2,2);

        //my variables
        vector<float>templ_descriptor;
        vector<float>p_descriptor;
        Mat templ_gray,p_gray,iMatches;
        vector<DMatch> matches;

        //convert to gray
        cvtColor(lastSeenObject,templ_gray,CV_BGR2GRAY);
        cvtColor(patch,p_gray,CV_BGR2GRAY);

        //create hog object
        HOGDescriptor hog(Size(templ_gray.cols/cellSize.width*cellSize.width,templ_gray.rows/cellSize.height*cellSize.height),
                Size(blockSize.height*cellSize.height,blockSize.width*cellSize.width),
                Size(cellSize.height,cellSize.width),
                cellSize,
                nbins);
        // gives --> winSize [32 x 16],  blockSize [16 x 16],  blockStride [8 x 8],  cellSize [8 x 8]

        //compute the descriptor of the car
        hog.compute(templ_gray,templ_descriptor, Size(cellSize.height,cellSize.width), Size( 0, 0 ));
        //templ_descriptor.size() = 108, containing floats between 0 and 1

        //compute the descriptor of the patch
        hog.compute(p_gray,p_descriptor, Size(cellSize.height,cellSize.width), Size( 0, 0 ));
        //p_descriptor.size() = 27540, containing floats between 0 and 1

        //compare the descriptors
        double err=0;
        double min_err = -1;
        int idx=-1;
        for (unsigned int i =0;i<p_descriptor.size();i++)
        {
            if(i%templ_descriptor.size()==0 && i!=0) // iterate the computation of error over the templ_descriptor size
            {
                if(err<min_err || min_err ==-1)
                {
                    min_err = err;
                    idx = i-nbins;
                }
                err = 0;
            }
            //euclidean error distance accumulator between each component of the histogram
            err += abs(p_descriptor[i] - templ_descriptor[i%templ_descriptor.size()]);
        }

        // we get idx = 11655 and err = 5.34021

        //convert vector idx in x,y coordonates in the patch
        int row= static_cast<int>(idx/patch.cols);
        int col = idx%patch.cols;

        //show the result
        Rect2f found_object(col,row,hog.winSize.width,hog.winSize.height); // [32 x 16 from (105, 77)]
        rectangle(patch,found_object,Scalar(0,0,255));
        imshow("result",patch);
        waitKey(500000);

        return 1;
}

我的结果