diff --git a/modules/ml/include/opencv2/ml.hpp b/modules/ml/include/opencv2/ml.hpp
index aca013e090..42b90d22d9 100644
--- a/modules/ml/include/opencv2/ml.hpp
+++ b/modules/ml/include/opencv2/ml.hpp
@@ -518,6 +518,8 @@ public:
     virtual CvSVMParams get_params() const { return params; }
     CV_WRAP virtual void clear();
 
+    virtual const CvSVMDecisionFunc* get_decision_function() const { return decision_func; }
+
     static CvParamGrid get_default_grid( int param_id );
 
     virtual void write( CvFileStorage* storage, const char* name ) const;
diff --git a/modules/ml/src/svm.cpp b/modules/ml/src/svm.cpp
index 84de4cd797..f2d3c33ab8 100644
--- a/modules/ml/src/svm.cpp
+++ b/modules/ml/src/svm.cpp
@@ -1245,7 +1245,6 @@ const float* CvSVM::get_support_vector(int i) const
     return sv && (unsigned)i < (unsigned)sv_total ? sv[i] : 0;
 }
 
-
 bool CvSVM::set_params( const CvSVMParams& _params )
 {
     bool ok = false;
diff --git a/samples/cpp/train_HOG.cpp b/samples/cpp/train_HOG.cpp
new file mode 100644
index 0000000000..3f21206a18
--- /dev/null
+++ b/samples/cpp/train_HOG.cpp
@@ -0,0 +1,461 @@
+#include <opencv2/opencv.hpp>
+
+#include <string>
+#include <iostream>
+#include <fstream>
+#include <vector>
+
+#include <time.h>
+
+using namespace cv;
+using namespace std;
+
+void get_svm_detector(const 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 sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size );
+Mat get_hogdescriptor_visu(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 get_svm_detector(const SVM& svm, vector< float > & hog_detector )
+{
+    // get the number of variables
+    const int var_all = svm.get_var_count();
+    // get the number of support vectors
+    const int sv_total = svm.get_support_vector_count();
+    // get the decision function
+    const CvSVMDecisionFunc* decision_func = svm.get_decision_function();
+    // get the support vectors
+    const float** sv = new const float*[ sv_total ];
+    for( int i = 0 ; i < sv_total ; ++i )
+        sv[ i ] = svm.get_support_vector(i);
+
+    CV_Assert( var_all > 0 &&
+        sv_total > 0 &&
+        decision_func != 0 &&
+        decision_func->alpha != 0 &&
+        decision_func->sv_count == sv_total );
+
+    float svi = 0.f;
+
+    hog_detector.clear(); //clear stuff in vector.
+    hog_detector.reserve( var_all + 1 ); //reserve place for memory efficiency.
+
+     /**
+    * hog_detector^i = \sum_j support_vector_j^i * \alpha_j
+    * hog_detector^dim = -\rho
+    */
+   for( int i = 0 ; i < var_all ; ++i )
+    {
+        svi = 0.f;
+        for( int j = 0 ; j < sv_total ; ++j )
+        {
+            if( decision_func->sv_index != NULL ) // sometime the sv_index isn't store on YML/XML.
+                svi += (float)( sv[decision_func->sv_index[j]][i] * decision_func->alpha[ j ] );
+            else
+                svi += (float)( sv[j][i] * decision_func->alpha[ j ] );
+        }
+        hog_detector.push_back( svi );
+    }
+    hog_detector.push_back( (float)-decision_func->rho );
+
+    delete[] sv;
+}
+
+
+/*
+* 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 )
+{
+    //--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 )
+    {
+        CV_Assert( itr->cols == 1 ||
+            itr->rows == 1 );
+        if( itr->cols == 1 )
+        {
+            transpose( *(itr), tmp );
+            tmp.copyTo( trainData.row( i ) );
+        }
+        else if( itr->rows == 1 )
+        {
+            itr->copyTo( trainData.row( i ) );
+        }
+    }
+}
+
+void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst )
+{
+    string line;
+    ifstream file;
+
+    file.open( (prefix+filename).c_str() );
+    if( !file.is_open() )
+    {
+        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 == "" ) // no more file to read
+        {
+            end_of_parsing = true;
+            break;
+        }
+        Mat img = imread( (prefix+line).c_str() ); // load the image
+        if( !img.data ) // invalid image, just skip it.
+            continue;
+#ifdef _DEBUG
+        imshow( "image", img );
+        waitKey( 10 );
+#endif
+        img_lst.push_back( img.clone() );
+    }
+}
+
+void sample_neg( 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.height;
+
+    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 )
+    {
+        box.x = rand() % (img->cols - size_x);
+        box.y = rand() % (img->rows - size_y);
+        Mat roi = (*img)(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(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)), CV_RGB(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)), CV_RGB(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 )
+{
+    HOGDescriptor hog;
+    hog.winSize = size;
+    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 )
+    {
+        cvtColor( *img, gray, COLOR_BGR2GRAY );
+        hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ), location );
+        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 )
+{
+    SVM svm;
+
+    /* Default values to train SVM */
+    SVMParams params;
+    params.coef0 = 0.0;
+    params.degree = 3;
+    params.term_crit.epsilon = 1e-3;
+    params.gamma = 0;
+    params.kernel_type = SVM::LINEAR;
+    params.nu = 0.5;
+    params.p = 0.1; // for EPSILON_SVR, epsilon in loss function?
+    params.C = 0.01; // From paper, soft classifier
+    params.svm_type = SVM::EPS_SVR; // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
+
+    Mat train_data;
+    convert_to_ml( gradient_lst, train_data );
+
+    clog << "Start training...";
+    svm.train( train_data, Mat( labels ), Mat(), Mat(), params );
+    clog << "...[done]" << endl;
+
+    svm.save( "my_people_detector.yml" );
+}
+
+void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color )
+{
+    if( !locations.empty() )
+    {
+        vector< Rect >::const_iterator loc = locations.begin();
+        vector< Rect >::const_iterator end = locations.end();
+        for( ; loc != end ; ++loc )
+        {
+            rectangle( img, *loc, color, 2 );
+        }
+    }
+}
+
+void test_it( const Size & size )
+{
+    char key = 27;
+    Scalar reference( 0, 255, 0 );
+    Scalar trained( 0, 0, 255 );
+    Mat img, draw;
+    SVM svm;
+    HOGDescriptor hog;
+    HOGDescriptor my_hog;
+    my_hog.winSize = size;
+    VideoCapture video;
+    vector< Rect > locations;
+
+    // Load the trained SVM.
+    svm.load( "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.data )
+            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;
+    }
+}
+
+int main( int argc, char** argv )
+{
+    if( argc != 4 )
+    {
+        cout << "Wrong number of parameters." << endl
+            << "Usage: " << argv[0] << " pos_dir pos.lst neg_dir neg.lst" << endl
+            << "example: " << argv[0] << " /INRIA_dataset/ Train/pos.lst /INRIA_dataset/ Train/neg.lst" << endl;
+        exit( -1 );
+    }
+    vector< Mat > pos_lst;
+    vector< Mat > full_neg_lst;
+    vector< Mat > neg_lst;
+    vector< Mat > gradient_lst;
+    vector< int > labels;
+
+    load_images( argv[1], argv[2], pos_lst );
+    labels.assign( pos_lst.size(), +1 );
+    const unsigned int old = (unsigned int)labels.size();
+    load_images( argv[3], argv[4], full_neg_lst );
+    sample_neg( full_neg_lst, neg_lst, Size( 96,160 ) );
+    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 ) );
+
+    train_svm( gradient_lst, labels );
+
+    test_it( Size( 96, 160 ) ); // change with your parameters
+
+    return 0;
+}