diff --git a/samples/cpp/train_HOG.cpp b/samples/cpp/train_HOG.cpp
index 900637b6b3..4d1dcf8e69 100644
--- a/samples/cpp/train_HOG.cpp
+++ b/samples/cpp/train_HOG.cpp
@@ -1,34 +1,30 @@
-#include <opencv2/opencv.hpp>
+#include "opencv2/imgproc.hpp"
+#include "opencv2/highgui.hpp"
+#include "opencv2/ml.hpp"
+#include "opencv2/objdetect.hpp"
 
-#include <string>
 #include <iostream>
-#include <fstream>
-#include <vector>
-
 #include <time.h>
 
 using namespace cv;
 using namespace cv::ml;
 using namespace std;
 
-void get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector );
-void convert_to_ml(const std::vector< cv::Mat > & train_samples, cv::Mat& trainData );
-void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst );
+void get_svm_detector( const Ptr< SVM > & svm, vector< float > & hog_detector );
+void convert_to_ml( const std::vector< Mat > & train_samples, Mat& trainData );
+void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages );
 void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size );
-Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorValues, const Size & size );
-void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, const Size & size );
-void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels );
-void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color );
-void test_it( 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_detector(const Ptr<SVM>& svm, vector< float > & hog_detector )
+void get_svm_detector( 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);
+    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.) ||
@@ -37,70 +33,59 @@ void get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector )
     hog_detector.clear();
 
     hog_detector.resize(sv.cols + 1);
-    memcpy(&hog_detector[0], sv.ptr(), sv.cols*sizeof(hog_detector[0]));
+    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_ml(const std::vector< cv::Mat > & train_samples, cv::Mat& trainData )
+void convert_to_ml( 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 );
-    cv::Mat tmp(1, cols, CV_32FC1); //< used for transposition if needed
-    trainData = cv::Mat(rows, cols, CV_32FC1 );
-    vector< Mat >::const_iterator itr = train_samples.begin();
-    vector< Mat >::const_iterator end = train_samples.end();
-    for( int i = 0 ; itr != end ; ++itr, ++i )
+    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( itr->cols == 1 ||
-            itr->rows == 1 );
-        if( itr->cols == 1 )
+        CV_Assert( train_samples[i].cols == 1 || train_samples[i].rows == 1 );
+
+        if( train_samples[i].cols == 1 )
         {
-            transpose( *(itr), tmp );
-            tmp.copyTo( trainData.row( i ) );
+            transpose( train_samples[i], tmp );
+            tmp.copyTo( trainData.row( (int)i ) );
         }
-        else if( itr->rows == 1 )
+        else if( train_samples[i].rows == 1 )
         {
-            itr->copyTo( trainData.row( i ) );
+            train_samples[i].copyTo( trainData.row( (int)i ) );
         }
     }
 }
 
-void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst )
+void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages = false )
 {
-    string line;
-    ifstream file;
+    vector< String > files;
+    glob( dirname, files );
 
-    file.open( (prefix+filename).c_str() );
-    if( !file.is_open() )
+    for ( size_t i = 0; i < files.size(); ++i )
     {
-        cerr << "Unable to open the list of images from " << filename << " filename." << endl;
-        exit( -1 );
-    }
-
-    bool end_of_parsing = false;
-    while( !end_of_parsing )
-    {
-        getline( file, line );
-        if( line.empty() ) // no more file to read
+        Mat img = imread( files[i] ); // load the image
+        if ( img.empty() )            // invalid image, skip it.
         {
-            end_of_parsing = true;
-            break;
-        }
-        Mat img = imread( (prefix+line).c_str() ); // load the image
-        if( img.empty() ) // invalid image, just skip it.
+            cout << files[i] << " is invalid!" << endl;
             continue;
-#ifdef _DEBUG
-        imshow( "image", img );
-        waitKey( 10 );
-#endif
-        img_lst.push_back( img.clone() );
+        }
+
+        if ( showImages )
+        {
+            imshow( "image", img );
+            waitKey( 1 );
+        }
+        img_lst.push_back( img );
     }
 }
 
@@ -115,331 +100,277 @@ void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, co
 
     srand( (unsigned int)time( NULL ) );
 
-    vector< Mat >::const_iterator img = full_neg_lst.begin();
-    vector< Mat >::const_iterator end = full_neg_lst.end();
-    for( ; img != end ; ++img )
+    for ( size_t i = 0; i < full_neg_lst.size(); i++ )
     {
-        box.x = rand() % (img->cols - size_x);
-        box.y = rand() % (img->rows - size_y);
-        Mat roi = (*img)(box);
+        box.x = rand() % ( full_neg_lst[i].cols - size_x );
+        box.y = rand() % ( full_neg_lst[i].rows - size_y );
+        Mat roi = full_neg_lst[i]( box );
         neg_lst.push_back( roi.clone() );
-#ifdef _DEBUG
-        imshow( "img", roi.clone() );
-        waitKey( 10 );
-#endif
     }
 }
 
-// From http://www.juergenwiki.de/work/wiki/doku.php?id=public:hog_descriptor_computation_and_visualization
-Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorValues, const Size & size )
-{
-    const int DIMX = size.width;
-    const int DIMY = size.height;
-    float zoomFac = 3;
-    Mat visu;
-    resize(color_origImg, visu, Size( (int)(color_origImg.cols*zoomFac), (int)(color_origImg.rows*zoomFac) ) );
-
-    int cellSize        = 8;
-    int gradientBinSize = 9;
-    float radRangeForOneBin = (float)(CV_PI/(float)gradientBinSize); // dividing 180 into 9 bins, how large (in rad) is one bin?
-
-    // prepare data structure: 9 orientation / gradient strenghts for each cell
-    int cells_in_x_dir = DIMX / cellSize;
-    int cells_in_y_dir = DIMY / cellSize;
-    float*** gradientStrengths = new float**[cells_in_y_dir];
-    int** cellUpdateCounter   = new int*[cells_in_y_dir];
-    for (int y=0; y<cells_in_y_dir; y++)
-    {
-        gradientStrengths[y] = new float*[cells_in_x_dir];
-        cellUpdateCounter[y] = new int[cells_in_x_dir];
-        for (int x=0; x<cells_in_x_dir; x++)
-        {
-            gradientStrengths[y][x] = new float[gradientBinSize];
-            cellUpdateCounter[y][x] = 0;
-
-            for (int bin=0; bin<gradientBinSize; bin++)
-                gradientStrengths[y][x][bin] = 0.0;
-        }
-    }
-
-    // nr of blocks = nr of cells - 1
-    // since there is a new block on each cell (overlapping blocks!) but the last one
-    int blocks_in_x_dir = cells_in_x_dir - 1;
-    int blocks_in_y_dir = cells_in_y_dir - 1;
-
-    // compute gradient strengths per cell
-    int descriptorDataIdx = 0;
-    int cellx = 0;
-    int celly = 0;
-
-    for (int blockx=0; blockx<blocks_in_x_dir; blockx++)
-    {
-        for (int blocky=0; blocky<blocks_in_y_dir; blocky++)
-        {
-            // 4 cells per block ...
-            for (int cellNr=0; cellNr<4; cellNr++)
-            {
-                // compute corresponding cell nr
-                cellx = blockx;
-                celly = blocky;
-                if (cellNr==1) celly++;
-                if (cellNr==2) cellx++;
-                if (cellNr==3)
-                {
-                    cellx++;
-                    celly++;
-                }
-
-                for (int bin=0; bin<gradientBinSize; bin++)
-                {
-                    float gradientStrength = descriptorValues[ descriptorDataIdx ];
-                    descriptorDataIdx++;
-
-                    gradientStrengths[celly][cellx][bin] += gradientStrength;
-
-                } // for (all bins)
-
-
-                // note: overlapping blocks lead to multiple updates of this sum!
-                // we therefore keep track how often a cell was updated,
-                // to compute average gradient strengths
-                cellUpdateCounter[celly][cellx]++;
-
-            } // for (all cells)
-
-
-        } // for (all block x pos)
-    } // for (all block y pos)
-
-
-    // compute average gradient strengths
-    for (celly=0; celly<cells_in_y_dir; celly++)
-    {
-        for (cellx=0; cellx<cells_in_x_dir; cellx++)
-        {
-
-            float NrUpdatesForThisCell = (float)cellUpdateCounter[celly][cellx];
-
-            // compute average gradient strenghts for each gradient bin direction
-            for (int bin=0; bin<gradientBinSize; bin++)
-            {
-                gradientStrengths[celly][cellx][bin] /= NrUpdatesForThisCell;
-            }
-        }
-    }
-
-    // draw cells
-    for (celly=0; celly<cells_in_y_dir; celly++)
-    {
-        for (cellx=0; cellx<cells_in_x_dir; cellx++)
-        {
-            int drawX = cellx * cellSize;
-            int drawY = celly * cellSize;
-
-            int mx = drawX + cellSize/2;
-            int my = drawY + cellSize/2;
-
-            rectangle(visu, Point((int)(drawX*zoomFac), (int)(drawY*zoomFac)), Point((int)((drawX+cellSize)*zoomFac), (int)((drawY+cellSize)*zoomFac)), Scalar(100,100,100), 1);
-
-            // draw in each cell all 9 gradient strengths
-            for (int bin=0; bin<gradientBinSize; bin++)
-            {
-                float currentGradStrength = gradientStrengths[celly][cellx][bin];
-
-                // no line to draw?
-                if (currentGradStrength==0)
-                    continue;
-
-                float currRad = bin * radRangeForOneBin + radRangeForOneBin/2;
-
-                float dirVecX = cos( currRad );
-                float dirVecY = sin( currRad );
-                float maxVecLen = (float)(cellSize/2.f);
-                float scale = 2.5; // just a visualization scale, to see the lines better
-
-                // compute line coordinates
-                float x1 = mx - dirVecX * currentGradStrength * maxVecLen * scale;
-                float y1 = my - dirVecY * currentGradStrength * maxVecLen * scale;
-                float x2 = mx + dirVecX * currentGradStrength * maxVecLen * scale;
-                float y2 = my + dirVecY * currentGradStrength * maxVecLen * scale;
-
-                // draw gradient visualization
-                line(visu, Point((int)(x1*zoomFac),(int)(y1*zoomFac)), Point((int)(x2*zoomFac),(int)(y2*zoomFac)), Scalar(0,255,0), 1);
-
-            } // for (all bins)
-
-        } // for (cellx)
-    } // for (celly)
-
-
-    // don't forget to free memory allocated by helper data structures!
-    for (int y=0; y<cells_in_y_dir; y++)
-    {
-        for (int x=0; x<cells_in_x_dir; x++)
-        {
-            delete[] gradientStrengths[y][x];
-        }
-        delete[] gradientStrengths[y];
-        delete[] cellUpdateCounter[y];
-    }
-    delete[] gradientStrengths;
-    delete[] cellUpdateCounter;
-
-    return visu;
-
-} // get_hogdescriptor_visu
-
-void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, const Size & size )
+void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst )
 {
     HOGDescriptor hog;
-    hog.winSize = size;
+    hog.winSize = wsize;
+
+    Rect r = Rect( 0, 0, wsize.width, wsize.height );
+    r.x += ( img_lst[0].cols - r.width ) / 2;
+    r.y += ( img_lst[0].rows - r.height ) / 2;
+
     Mat gray;
-    vector< Point > location;
     vector< float > descriptors;
 
-    vector< Mat >::const_iterator img = img_lst.begin();
-    vector< Mat >::const_iterator end = img_lst.end();
-    for( ; img != end ; ++img )
+    for( size_t i=0 ; i< img_lst.size(); i++ )
     {
-        cvtColor( *img, gray, COLOR_BGR2GRAY );
-        hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ), location );
+        cvtColor( img_lst[i](r), gray, COLOR_BGR2GRAY );
+        hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) );
         gradient_lst.push_back( Mat( descriptors ).clone() );
-#ifdef _DEBUG
-        imshow( "gradient", get_hogdescriptor_visu( img->clone(), descriptors, size ) );
-        waitKey( 10 );
-#endif
     }
 }
 
-void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels )
+int test_trained_detector( String obj_det_filename, String test_dir, String videofilename )
 {
+    cout << "Testing trained detector..." << endl;
+    HOGDescriptor hog;
+    hog.load( obj_det_filename );
 
-    Mat train_data;
-    convert_to_ml( gradient_lst, train_data );
+    vector< String > files;
+    glob( test_dir, files );
 
-    clog << "Start training...";
-    Ptr<SVM> svm = SVM::create();
-    /* Default values to train SVM */
-    svm->setCoef0(0.0);
-    svm->setDegree(3);
-    svm->setTermCriteria(TermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, 1e-3 ));
-    svm->setGamma(0);
-    svm->setKernel(SVM::LINEAR);
-    svm->setNu(0.5);
-    svm->setP(0.1); // for EPSILON_SVR, epsilon in loss function?
-    svm->setC(0.01); // From paper, soft classifier
-    svm->setType(SVM::EPS_SVR); // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
-    svm->train(train_data, ROW_SAMPLE, Mat(labels));
-    clog << "...[done]" << endl;
+    int delay = 0;
+    VideoCapture cap;
 
-    svm->save( "my_people_detector.yml" );
-}
-
-void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color )
-{
-    if( !locations.empty() )
+    if ( videofilename != "" )
     {
-        vector< Rect >::const_iterator loc = locations.begin();
-        vector< Rect >::const_iterator end = locations.end();
-        for( ; loc != end ; ++loc )
+        cap.open( videofilename );
+    }
+
+    obj_det_filename = "testing " + obj_det_filename;
+    namedWindow( obj_det_filename, WINDOW_NORMAL );
+
+    for( size_t i=0;; i++ )
+    {
+        Mat img;
+
+        if ( cap.isOpened() )
         {
-            rectangle( img, *loc, color, 2 );
+            cap >> img;
+            delay = 1;
+        }
+        else if( i < files.size() )
+        {
+            img = imread( files[i] );
+        }
+
+        if ( img.empty() )
+        {
+            return 0;
+        }
+
+        vector< Rect > detections;
+        vector< double > foundWeights;
+
+        hog.detectMultiScale( img, detections, foundWeights );
+        for ( size_t j = 0; j < detections.size(); j++ )
+        {
+            Scalar color = Scalar( 0, foundWeights[j] * foundWeights[j] * 200, 0 );
+            rectangle( img, detections[j], color, img.cols / 400 + 1 );
+        }
+
+        imshow( obj_det_filename, img );
+
+        if( 27 == waitKey( delay ) )
+        {
+            return 0;
         }
     }
-}
-
-void test_it( const Size & size )
-{
-    char key = 27;
-    Scalar reference( 0, 255, 0 );
-    Scalar trained( 0, 0, 255 );
-    Mat img, draw;
-    Ptr<SVM> svm;
-    HOGDescriptor hog;
-    HOGDescriptor my_hog;
-    my_hog.winSize = size;
-    VideoCapture video;
-    vector< Rect > locations;
-
-    // Load the trained SVM.
-    svm = StatModel::load<SVM>( "my_people_detector.yml" );
-    // Set the trained svm to my_hog
-    vector< float > hog_detector;
-    get_svm_detector( svm, hog_detector );
-    my_hog.setSVMDetector( hog_detector );
-    // Set the people detector.
-    hog.setSVMDetector( hog.getDefaultPeopleDetector() );
-    // Open the camera.
-    video.open(0);
-    if( !video.isOpened() )
-    {
-        cerr << "Unable to open the device 0" << endl;
-        exit( -1 );
-    }
-
-    bool end_of_process = false;
-    while( !end_of_process )
-    {
-        video >> img;
-        if( img.empty() )
-            break;
-
-        draw = img.clone();
-
-        locations.clear();
-        hog.detectMultiScale( img, locations );
-        draw_locations( draw, locations, reference );
-
-        locations.clear();
-        my_hog.detectMultiScale( img, locations );
-        draw_locations( draw, locations, trained );
-
-        imshow( "Video", draw );
-        key = (char)waitKey( 10 );
-        if( 27 == key )
-            end_of_process = true;
-    }
+    return 0;
 }
 
 int main( int argc, char** argv )
 {
-    cv::CommandLineParser parser(argc, argv, "{help h|| show help message}"
-            "{pd||pos_dir}{p||pos.lst}{nd||neg_dir}{n||neg.lst}");
-    if (parser.has("help"))
+    const char* keys =
+    {
+        "{help h|     | show help message}"
+        "{pd    |     | path of directory contains possitive images}"
+        "{nd    |     | path of directory contains negative images}"
+        "{td    |     | path of directory contains test images}"
+        "{tv    |     | test video file name}"
+        "{dw    |     | width of the detector}"
+        "{dh    |     | height of the detector}"
+        "{d     |false| train twice}"
+        "{t     |false| test a trained detector}"
+        "{v     |false| visualize training steps}"
+        "{fn    |my_detector.yml| file name of trained SVM}"
+    };
+
+    CommandLineParser parser( argc, argv, keys );
+
+    if ( parser.has( "help" ) )
     {
         parser.printMessage();
-        exit(0);
+        exit( 0 );
     }
-    vector< Mat > pos_lst;
-    vector< Mat > full_neg_lst;
-    vector< Mat > neg_lst;
-    vector< Mat > gradient_lst;
-    vector< int > labels;
-    string pos_dir = parser.get<string>("pd");
-    string pos = parser.get<string>("p");
-    string neg_dir = parser.get<string>("nd");
-    string neg = parser.get<string>("n");
-    if( pos_dir.empty() || pos.empty() || neg_dir.empty() || neg.empty() )
+
+    String pos_dir = parser.get< String >( "pd" );
+    String neg_dir = parser.get< String >( "nd" );
+    String test_dir = parser.get< String >( "td" );
+    String obj_det_filename = parser.get< String >( "fn" );
+    String videofilename = parser.get< String >( "tv" );
+    int detector_width = parser.get< int >( "dw" );
+    int detector_height = parser.get< int >( "dh" );
+    bool test_detector = parser.get< bool >( "t" );
+    bool train_twice = parser.get< bool >( "d" );
+    bool visualization = parser.get< bool >( "v" );
+
+    if ( test_detector )
     {
-        cout << "Wrong number of parameters." << endl
-            << "Usage: " << argv[0] << " --pd=pos_dir -p=pos.lst --nd=neg_dir -n=neg.lst" << endl
-            << "example: " << argv[0] << " --pd=/INRIA_dataset/ -p=Train/pos.lst --nd=/INRIA_dataset/ -n=Train/neg.lst" << endl;
-        exit( -1 );
+        test_trained_detector( obj_det_filename, test_dir, videofilename );
+        exit( 0 );
     }
-    load_images( pos_dir, pos, pos_lst );
+
+    if( pos_dir.empty() || neg_dir.empty() )
+    {
+        parser.printMessage();
+        cout << "Wrong number of parameters.\n\n"
+             << "Example command line:\n" << argv[0] << " -pd=/INRIAPerson/96X160H96/Train/pos -nd=/INRIAPerson/neg -td=/INRIAPerson/Test/pos -fn=HOGpedestrian96x160.yml -d\n"
+             << "\nExample command line for testing trained detector:\n" << argv[0] << " -t -dw=96 -dh=160 -fn=HOGpedestrian96x160.yml -td=/INRIAPerson/Test/pos";
+        exit( 1 );
+    }
+
+    vector< Mat > pos_lst, full_neg_lst, neg_lst, gradient_lst;
+    vector< int > labels;
+
+    clog << "Positive images are being loaded..." ;
+    load_images( pos_dir, pos_lst, visualization );
+    if ( pos_lst.size() > 0 )
+    {
+        clog << "...[done]" << endl;
+    }
+    else
+    {
+        clog << "no image in " << pos_dir <<endl;
+        return 1;
+    }
+
+    Size pos_image_size = pos_lst[0].size();
+
+    for ( size_t i = 0; i < pos_lst.size(); ++i )
+    {
+        if( pos_lst[i].size() != pos_image_size )
+        {
+            cout << "All positive images should be same size!" << endl;
+            exit( 1 );
+        }
+    }
+
+    pos_image_size = pos_image_size / 8 * 8;
+
+    if ( detector_width && detector_height )
+    {
+        pos_image_size = Size( detector_width, detector_height );
+    }
+
     labels.assign( pos_lst.size(), +1 );
     const unsigned int old = (unsigned int)labels.size();
-    load_images( neg_dir, neg, full_neg_lst );
-    sample_neg( full_neg_lst, neg_lst, Size( 96,160 ) );
+
+    clog << "Negative images are being loaded...";
+    load_images( neg_dir, full_neg_lst, false );
+    sample_neg( full_neg_lst, neg_lst, pos_image_size );
+    clog << "...[done]" << endl;
+
     labels.insert( labels.end(), neg_lst.size(), -1 );
     CV_Assert( old < labels.size() );
 
-    compute_hog( pos_lst, gradient_lst, Size( 96, 160 ) );
-    compute_hog( neg_lst, gradient_lst, Size( 96, 160 ) );
+    clog << "Histogram of Gradients are being calculated for positive images...";
+    computeHOGs( pos_image_size, pos_lst, gradient_lst );
+    clog << "...[done]" << endl;
 
-    train_svm( gradient_lst, labels );
+    clog << "Histogram of Gradients are being calculated for negative images...";
+    computeHOGs( pos_image_size, neg_lst, gradient_lst );
+    clog << "...[done]" << endl;
 
-    test_it( Size( 96, 160 ) ); // change with your parameters
+    Mat train_data;
+    convert_to_ml( gradient_lst, train_data );
+
+    clog << "Training SVM...";
+    Ptr< SVM > svm = SVM::create();
+    /* Default values to train SVM */
+    svm->setCoef0( 0.0 );
+    svm->setDegree( 3 );
+    svm->setTermCriteria( TermCriteria( CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, 1e-3 ) );
+    svm->setGamma( 0 );
+    svm->setKernel( SVM::LINEAR );
+    svm->setNu( 0.5 );
+    svm->setP( 0.1 ); // for EPSILON_SVR, epsilon in loss function?
+    svm->setC( 0.01 ); // From paper, soft classifier
+    svm->setType( SVM::EPS_SVR ); // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
+    svm->train( train_data, ROW_SAMPLE, Mat( labels ) );
+    clog << "...[done]" << endl;
+
+    if ( train_twice )
+    {
+        clog << "Testing trained detector on negative images. This may take a few minutes...";
+        HOGDescriptor my_hog;
+        my_hog.winSize = pos_image_size;
+
+        // Set the trained svm to my_hog
+        vector< float > hog_detector;
+        get_svm_detector( svm, hog_detector );
+        my_hog.setSVMDetector( hog_detector );
+
+        vector< Rect > detections;
+        vector< double > foundWeights;
+
+        for ( size_t i = 0; i < full_neg_lst.size(); i++ )
+        {
+            my_hog.detectMultiScale( full_neg_lst[i], detections, foundWeights );
+            for ( size_t j = 0; j < detections.size(); j++ )
+            {
+                Mat detection = full_neg_lst[i]( detections[j] ).clone();
+                resize( detection, detection, pos_image_size );
+                neg_lst.push_back( detection );
+            }
+            if ( visualization )
+            {
+                for ( size_t j = 0; j < detections.size(); j++ )
+                {
+                    rectangle( full_neg_lst[i], detections[j], Scalar( 0, 255, 0 ), 2 );
+                }
+                imshow( "testing trained detector on negative images", full_neg_lst[i] );
+                waitKey( 5 );
+            }
+        }
+        clog << "...[done]" << endl;
+
+        labels.clear();
+        labels.assign( pos_lst.size(), +1 );
+        labels.insert( labels.end(), neg_lst.size(), -1);
+
+        gradient_lst.clear();
+        clog << "Histogram of Gradients are being calculated for positive images...";
+        computeHOGs( pos_image_size, pos_lst, gradient_lst );
+        clog << "...[done]" << endl;
+
+        clog << "Histogram of Gradients are being calculated for negative images...";
+        computeHOGs( pos_image_size, neg_lst, gradient_lst );
+        clog << "...[done]" << endl;
+
+        clog << "Training SVM again...";
+        convert_to_ml( gradient_lst, train_data );
+        svm->train( train_data, ROW_SAMPLE, Mat( labels ) );
+        clog << "...[done]" << endl;
+    }
+
+    vector< float > hog_detector;
+    get_svm_detector( svm, hog_detector );
+    HOGDescriptor hog;
+    hog.winSize = pos_image_size;
+    hog.setSVMDetector( hog_detector );
+    hog.save( obj_det_filename );
+
+    test_trained_detector( obj_det_filename, test_dir, videofilename );
 
     return 0;
 }