Improving human detection with openCV

I am testing a sample for human detection on openCV. After running on image ( original image available here ), this is my output:

I'm using the person detection sample that comes bundled with openCV (slightly modified to avoid Visual Studio bugs). This is the code that gets executed:

    // opencv-sample.cpp : Defines the entry point for the console application.
//

#include "stdafx.h"

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"

#include <stdio.h>
#include <string.h>
#include <ctype.h>

using namespace cv;
using namespace std;

// static void help()
// {
//     printf(
//             "\nDemonstrate the use of the HoG descriptor using\n"
//             "  HOGDescriptor::hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());\n"
//             "Usage:\n"
//             "./peopledetect (<image_filename> | <image_list>.txt)\n\n");
// }

int main(int argc, char** argv)
{
    Mat img;
    FILE* f = 0;
    char _filename[1024];

    if (argc == 1)
    {
        printf("Usage: peopledetect (<image_filename> | <image_list>.txt)\n");
        return 0;
    }
    img = imread(argv[1]);

    if (img.data)
    {
        strcpy_s(_filename, argv[1]);
    }
    else
    {
        fopen_s(&f, argv[1], "rt");
        if (!f)
        {
            fprintf(stderr, "ERROR: the specified file could not be loaded\n");
            return -1;
        }
    }

    HOGDescriptor hog;
    hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
    namedWindow("people detector", 1);

    for (;;)
    {
        char* filename = _filename;
        if (f)
        {
            if (!fgets(filename, (int)sizeof(_filename) - 2, f))
                break;
            //while(*filename && isspace(*filename))
            //  ++filename;
            if (filename[0] == '#')
                continue;
            int l = (int)strlen(filename);
            while (l > 0 && isspace(filename[l - 1]))
                --l;
            filename[l] = '\0';
            img = imread(filename);
        }
        printf("%s:\n", filename);
        if (!img.data)
            continue;

        fflush(stdout);
        vector<Rect> found, found_filtered;
        double t = (double)getTickCount();
        // run the detector with default parameters. to get a higher hit-rate
        // (and more false alarms, respectively), decrease the hitThreshold and
        // groupThreshold (set groupThreshold to 0 to turn off the grouping completely).
        hog.detectMultiScale(img, found, 0, Size(8, 8), Size(32, 32), 1.05, 2);
        t = (double)getTickCount() - t;
        printf("tdetection time = %gms\n", t*1000. / cv::getTickFrequency());
        size_t i, j;
        for (i = 0; i < found.size(); i++)
        {
            Rect r = found[i];
            for (j = 0; j < found.size(); j++)
                if (j != i && (r & found[j]) == r)
                    break;
            if (j == found.size())
                found_filtered.push_back(r);
        }
        for (i = 0; i < found_filtered.size(); i++)
        {
            Rect r = found_filtered[i];
            // the HOG detector returns slightly larger rectangles than the real objects.
            // so we slightly shrink the rectangles to get a nicer output.
            r.x += cvRound(r.width*0.1);
            r.width = cvRound(r.width*0.8);
            r.y += cvRound(r.height*0.07);
            r.height = cvRound(r.height*0.8);
            rectangle(img, r.tl(), r.br(), cv::Scalar(0, 255, 0), 3);
        }
        imshow("people detector", img);
        imwrite("detected_ppl.jpg", img);
        int c = waitKey(0) & 255;
        if (c == 'q' || c == 'Q' || !f)
            break;
    }
    if (f)
        fclose(f);
    return 0;
}

      

        
        
        
      

    

I would like to improve on this result, where I can detect at least 9 out of 11 people in this image. How can I improve this result? Do I need to train a separate SVM? Or is there a better library I can use? Or do I need to resort to deep learning?