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Now I'm using CascadeClassifier, and yeah! I can detect human now!

This is my full code:

#include"stdafx.h"
#include<vector>
#include<iostream>
#include<opencv2/opencv.hpp>
#include<opencv2/core/core.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/objdetect/objdetect.hpp>

int main(int argc, char *argv[])
{
    cv::Mat frame;                                              
    cv::Mat fg;     
    cv::Mat blurred;
    cv::Mat thresholded;
    cv::Mat gray;
    cv::Mat blob;
    cv::Mat bgmodel;                                            
    cv::namedWindow("Frame");   
    cv::namedWindow("Background Model");
    cv::namedWindow("Blob");
    cv::VideoCapture cap("campus3.avi");    

    cv::BackgroundSubtractorMOG2 bgs;      

    bgs.nmixtures = 3;
    bgs.history = 1000;
    bgs.varThresholdGen = 15;
    bgs.bShadowDetection = true;                            
    bgs.nShadowDetection = 0;                               
    bgs.fTau = 0.5;                                         

std::vector<std::vector<cv::Point>> contours;               

cv::CascadeClassifier body;
assert(human.load("hogcascade_pedestrians.xml"));

for(;;)
{
    cap >> frame;                                           

    cv::GaussianBlur(frame,blurred,cv::Size(3,3),0,0,cv::BORDER_DEFAULT);

    bgs.operator()(blurred,fg);                         
    bgs.getBackgroundImage(bgmodel);                                

    cv::threshold(fg,thresholded,70.0f,255,CV_THRESH_BINARY);

    cv::Mat elementCLOSE(5,5,CV_8U,cv::Scalar(255,255,255));
    cv::morphologyEx(thresholded,thresholded,cv::MORPH_CLOSE,elementCLOSE);

    cv::findContours(thresholded,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE);
    cv::cvtColor(thresholded,blob,CV_GRAY2RGB);
    cv::drawContours(blob,contours,-1,cv::Scalar(1),CV_FILLED,8);

    cv::cvtColor(frame,gray,CV_RGB2GRAY);
    cv::equalizeHist(gray, gray);

    int cmin = 50; 
    int cmax = 1000;
    std::vector<cv::Rect> rects;
    std::vector<std::vector<cv::Point>>::iterator itc=contours.begin();

    while (itc!=contours.end()) {   

            if (itc->size() > cmin && itc->size() < cmax){ 

                    human.detectMultiScale(gray, rects);
                    for (unsigned int i=0;i<rects.size();i++) {
                        cv::rectangle(frame, cv::Point(rects[i].x, rects[i].y),
                        cv::Point(rects[i].x+rects[i].width, rects[i].y+rects[i].height),
                        cv::Scalar(0, 255, 0));
                     }

                    ++itc;
                }else{++itc;}
    }

    cv::imshow("Frame",frame);
    cv::imshow("Background Model",bgmodel);
    cv::imshow("Blob",blob);
    if(cv::waitKey(30) >= 0) break;
}
return 0;

}

Now I'm using CascadeClassifier, and yeah! I can detect human now!

This is my full code:

#include"stdafx.h"
#include<vector>
#include<iostream>
#include<opencv2/opencv.hpp>
#include<opencv2/core/core.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/objdetect/objdetect.hpp>

int main(int argc, char *argv[])
{
    cv::Mat frame;                                              
    cv::Mat fg;     
    cv::Mat blurred;
    cv::Mat thresholded;
    cv::Mat gray;
    cv::Mat blob;
    cv::Mat bgmodel;                                            
    cv::namedWindow("Frame");   
    cv::namedWindow("Background Model");
    cv::namedWindow("Blob");
    cv::VideoCapture cap("campus3.avi");    

    cv::BackgroundSubtractorMOG2 bgs;      

    bgs.nmixtures = 3;
    bgs.history = 1000;
    bgs.varThresholdGen = 15;
    bgs.bShadowDetection = true;                            
    bgs.nShadowDetection = 0;                               
    bgs.fTau = 0.5;                                         

std::vector<std::vector<cv::Point>> contours;               

cv::CascadeClassifier body;
assert(human.load("hogcascade_pedestrians.xml"));

for(;;)
{
    cap >> frame;                                           

    cv::GaussianBlur(frame,blurred,cv::Size(3,3),0,0,cv::BORDER_DEFAULT);

    bgs.operator()(blurred,fg);                         
    bgs.getBackgroundImage(bgmodel);                                

    cv::threshold(fg,thresholded,70.0f,255,CV_THRESH_BINARY);

    cv::Mat elementCLOSE(5,5,CV_8U,cv::Scalar(255,255,255));
    cv::morphologyEx(thresholded,thresholded,cv::MORPH_CLOSE,elementCLOSE);

    cv::findContours(thresholded,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE);
    cv::cvtColor(thresholded,blob,CV_GRAY2RGB);
    cv::drawContours(blob,contours,-1,cv::Scalar(1),CV_FILLED,8);

    cv::cvtColor(frame,gray,CV_RGB2GRAY);
    cv::equalizeHist(gray, gray);

    int cmin = 50; 
    int cmax = 1000;
    std::vector<cv::Rect> rects;
    std::vector<std::vector<cv::Point>>::iterator itc=contours.begin();

    while (itc!=contours.end()) {   

            if (itc->size() > cmin && itc->size() < cmax){ 

                    human.detectMultiScale(gray, rects);
                    for (unsigned int i=0;i<rects.size();i++) {
                        cv::rectangle(frame, cv::Point(rects[i].x, rects[i].y),
                        cv::Point(rects[i].x+rects[i].width, rects[i].y+rects[i].height),
                        cv::Scalar(0, 255, 0));
                     }

                    ++itc;
                }else{++itc;}
    }

    cv::imshow("Frame",frame);
    cv::imshow("Background Model",bgmodel);
    cv::imshow("Blob",blob);
    if(cv::waitKey(30) >= 0) break;
}
 return 0;
}