train_HOG not able to detect all images.

edit

I picked the code for train_HOG.cpp from openCV site. I went through all the steps carefully, but not able to get the accuracy even in same resolution image (original image from which I created positive images.). Attaching output images and code here to analyse. I put red circle on area that was not found by HOG utility. I need the image should be recognized if source images is of different scale (e.g. 50%, 75%, 125%, 150%). Awaiting response/feed back/comments.

#include "stdafx.h"
#include "TrainHOGEx.h"


TrainHOGEx::TrainHOGEx()
{
}


TrainHOGEx::~TrainHOGEx()
{
}
int mainTestEx();
void get_svm_detectorex(const Ptr< SVM > & svm, vector< float > & hog_detector);
void convert_to_mlex(const std::vector< Mat > & train_samples, Mat& trainData);
void load_images(const String & dirname, vector< Mat > & img_lst, bool showImages);
void sample_negex(const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size);
void computeHOGs(const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst);
int test_trained_detector(String obj_det_filename, String test_dir, String videofilename);

void get_svm_detectorex(const Ptr< SVM >& svm, vector< float > & hog_detector)
{
    // get the support vectors
    Mat sv = svm->getSupportVectors();
    const int sv_total = sv.rows;
    // get the decision function
    Mat alpha, svidx;
    double rho = svm->getDecisionFunction(0, alpha, svidx);

    CV_Assert(alpha.total() == 1 && svidx.total() == 1 && sv_total == 1);
    CV_Assert((alpha.type() == CV_64F && alpha.at<double>(0) == 1.) ||
        (alpha.type() == CV_32F && alpha.at<float>(0) == 1.f));
    CV_Assert(sv.type() == CV_32F);
    hog_detector.clear();

    hog_detector.resize(sv.cols + 1);
    memcpy(&hog_detector[0], sv.ptr(), sv.cols * sizeof(hog_detector[0]));
    hog_detector[sv.cols] = (float)-rho;
}

/*
* Convert training/testing set to be used by OpenCV Machine Learning algorithms.
* TrainData is a matrix of size (#samples x max(#cols,#rows) per samples), in 32FC1.
* Transposition of samples are made if needed.
*/
void convert_to_mlex(const vector< Mat > & train_samples, Mat& trainData)
{
    //--Convert data
    const int rows = (int)train_samples.size();
    const int cols = (int)std::max(train_samples[0].cols, train_samples[0].rows);
    Mat tmp(1, cols, CV_32FC1); //< used for transposition if needed
    trainData = Mat(rows, cols, CV_32FC1);

    for (size_t i = 0; i < train_samples.size(); ++i)
    {
        CV_Assert(train_samples[i].cols == 1 || train_samples[i].rows == 1);

        if (train_samples[i].cols == 1)
        {
            transpose(train_samples[i], tmp);
            tmp.copyTo(trainData.row((int)i));
        }
        else if (train_samples[i].rows == 1)
        {
            train_samples[i].copyTo(trainData.row((int)i));
        }
    }
}

void load_images(const String & dirname, vector< Mat > & img_lst, bool showImages = false)
{
    try {
        vector< String > files;
        try {
            glob(dirname, files);
        }
        catch (...)
        {
            AfxMessageBox("exception");
        }

        for (size_t i = 0; i < files.size(); ++i)
        {
            Mat img = imread(files[i]); // load the image
            if (img.empty())            // invalid image, skip it.
            {
                cout << files[i] << " is invalid!" << endl;
                continue;
            }

            if (showImages)
            {
                imshow("image", img);
                waitKey(1);
            }
            img_lst.push_back(img);
        }
    }
    catch (...)
    {
        AfxMessageBox("exception");
    }
}

void sample_negex(const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size)
{
    Rect box;
    box.width = size.width;
    box.height = size.height;

    const int size_x = box.width;
    const int size_y = box ...