diff --git a/modules/tracking/samples/tutorial_customizing_cn_tracker.cpp b/modules/tracking/samples/tutorial_customizing_cn_tracker.cpp
new file mode 100644
index 000000000..3131be397
--- /dev/null
+++ b/modules/tracking/samples/tutorial_customizing_cn_tracker.cpp
@@ -0,0 +1,128 @@
+#include <opencv2/core/utility.hpp>
+#include <opencv2/tracking.hpp>
+#include <opencv2/videoio.hpp>
+#include <opencv2/highgui.hpp>
+#include <iostream>
+#include <cstring>
+
+using namespace std;
+using namespace cv;
+
+// prototype of the functino for feature extractor
+void sobelExtractor(const Mat img, const Rect roi, Mat& feat);
+
+int main( int argc, char** argv ){
+  // show help
+  if(argc<2){
+    cout<<
+      " Usage: tracker <video_name>\n"
+      " examples:\n"
+      " example_tracking_kcf Bolt/img/%04d.jpg\n"
+      " example_tracking_kcf faceocc2.webm\n"
+      << endl;
+    return 0;
+  }
+
+  // declares all required variables
+  Rect2d roi;
+  Mat frame;
+
+  //! [param]
+  TrackerKCF::Params param;
+  param.desc_pca = TrackerKCF::GRAY | TrackerKCF::CN;
+  param.desc_npca = 0;
+  param.compress_feature = true;
+  param.compressed_size = 2;
+  //! [param]
+
+  // create a tracker object
+  //! [create]
+  Ptr<TrackerKCF> tracker = TrackerKCF::createTracker(param);
+  //! [create]
+
+  //! [setextractor]
+  tracker->setFeatureExtractor(sobelExtractor);
+  //! [setextractor]
+
+  // set input video
+  std::string video = argv[1];
+  VideoCapture cap(video);
+
+  // get bounding box
+  cap >> frame;
+  roi=selectROI("tracker",frame);
+
+  //quit if ROI was not selected
+  if(roi.width==0 || roi.height==0)
+    return 0;
+
+  // initialize the tracker
+  tracker->init(frame,roi);
+
+  // perform the tracking process
+  printf("Start the tracking process, press ESC to quit.\n");
+  for ( ;; ){
+    // get frame from the video
+    cap >> frame;
+
+    // stop the program if no more images
+    if(frame.rows==0 || frame.cols==0)
+      break;
+
+    // update the tracking result
+    tracker->update(frame,roi);
+
+    // draw the tracked object
+    rectangle( frame, roi, Scalar( 255, 0, 0 ), 2, 1 );
+
+    // show image with the tracked object
+    imshow("tracker",frame);
+
+    //quit on ESC button
+    if(waitKey(1)==27)break;
+  }
+
+  return 0;
+}
+
+void sobelExtractor(const Mat img, const Rect roi, Mat& feat){
+    Mat sobel[2];
+    Mat patch;
+    Rect region=roi;
+
+    //! [insideimage]
+    // extract patch inside the image
+    if(roi.x<0){region.x=0;region.width+=roi.x;}
+    if(roi.y<0){region.y=0;region.height+=roi.y;}
+    if(roi.x+roi.width>img.cols)region.width=img.cols-roi.x;
+    if(roi.y+roi.height>img.rows)region.height=img.rows-roi.y;
+    if(region.width>img.cols)region.width=img.cols;
+    if(region.height>img.rows)region.height=img.rows;
+    //! [insideimage]
+
+    patch=img(region).clone();
+    cvtColor(patch,patch, CV_BGR2GRAY);
+
+    //! [padding]
+    // add some padding to compensate when the patch is outside image border
+    int addTop,addBottom, addLeft, addRight;
+    addTop=region.y-roi.y;
+    addBottom=(roi.height+roi.y>img.rows?roi.height+roi.y-img.rows:0);
+    addLeft=region.x-roi.x;
+    addRight=(roi.width+roi.x>img.cols?roi.width+roi.x-img.cols:0);
+
+    copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);
+    //! [padding]
+
+    //! [sobel]
+    Sobel(patch, sobel[0], CV_32F,1,0,1);
+    Sobel(patch, sobel[1], CV_32F,0,1,1);
+
+    merge(sobel,2,feat);
+    //! [sobel]
+
+    //! [postprocess]
+    feat.convertTo(feat,CV_64F);
+    feat=feat/255.0-0.5; // normalize to range -0.5 .. 0.5
+    //! [postprocess]
+}
diff --git a/modules/tracking/samples/tutorial_introduction_to_tracker.cpp b/modules/tracking/samples/tutorial_introduction_to_tracker.cpp
new file mode 100644
index 000000000..d9907fac3
--- /dev/null
+++ b/modules/tracking/samples/tutorial_introduction_to_tracker.cpp
@@ -0,0 +1,87 @@
+#include <opencv2/core/utility.hpp>
+#include <opencv2/tracking.hpp>
+#include <opencv2/videoio.hpp>
+#include <opencv2/highgui.hpp>
+#include <iostream>
+#include <cstring>
+
+using namespace std;
+using namespace cv;
+
+int main( int argc, char** argv ){
+  // show help
+  //! [help]
+  if(argc<2){
+    cout<<
+      " Usage: tracker <video_name>\n"
+      " examples:\n"
+      " example_tracking_kcf Bolt/img/%04d.jpg\n"
+      " example_tracking_kcf faceocc2.webm\n"
+      << endl;
+    return 0;
+  }
+  //! [help]
+
+  // declares all required variables
+  //! [vars]
+  Rect2d roi;
+  Mat frame;
+  //! [vars]
+
+  // create a tracker object
+  //! [create]
+  Ptr<Tracker> tracker = Tracker::create( "KCF" );
+  //! [create]
+
+  // set input video
+  //! [setvideo]
+  std::string video = argv[1];
+  VideoCapture cap(video);
+  //! [setvideo]
+
+  // get bounding box
+  //! [getframe]
+  cap >> frame;
+  //! [getframe]
+  //! [selectroi]
+  roi=selectROI("tracker",frame);
+  //! [selectroi]
+
+  //quit if ROI was not selected
+  if(roi.width==0 || roi.height==0)
+    return 0;
+
+  // initialize the tracker
+  //! [init]
+  tracker->init(frame,roi);
+  //! [init]
+
+  // perform the tracking process
+  printf("Start the tracking process, press ESC to quit.\n");
+  for ( ;; ){
+    // get frame from the video
+    cap >> frame;
+
+    // stop the program if no more images
+    if(frame.rows==0 || frame.cols==0)
+      break;
+
+    // update the tracking result
+    //! [update]
+    tracker->update(frame,roi);
+    //! [update]
+
+    //! [visualization]
+    // draw the tracked object
+    rectangle( frame, roi, Scalar( 255, 0, 0 ), 2, 1 );
+
+    // show image with the tracked object
+    imshow("tracker",frame);
+    //! [visualization]
+
+    //quit on ESC button
+    if(waitKey(1)==27)break;
+  }
+
+  return 0;
+}
diff --git a/modules/tracking/samples/tutorial_multitracker.cpp b/modules/tracking/samples/tutorial_multitracker.cpp
new file mode 100644
index 000000000..0d14c2087
--- /dev/null
+++ b/modules/tracking/samples/tutorial_multitracker.cpp
@@ -0,0 +1,99 @@
+/*----------------------------------------------
+ * Usage:
+ * example_tracking_multitracker <video_name> [algorithm]
+ *
+ * example:
+ * example_tracking_multitracker Bolt/img/%04d.jpg
+ * example_tracking_multitracker faceocc2.webm KCF
+ *--------------------------------------------------*/
+
+#include <opencv2/core/utility.hpp>
+#include <opencv2/tracking.hpp>
+#include <opencv2/videoio.hpp>
+#include <opencv2/highgui.hpp>
+#include <iostream>
+#include <cstring>
+#include <ctime>
+
+using namespace std;
+using namespace cv;
+
+int main( int argc, char** argv ){
+  // show help
+  if(argc<2){
+    cout<<
+      " Usage: example_tracking_multitracker <video_name> [algorithm]\n"
+      " examples:\n"
+      " example_tracking_multitracker Bolt/img/%04d.jpg\n"
+      " example_tracking_multitracker faceocc2.webm MEDIANFLOW\n"
+      << endl;
+    return 0;
+  }
+
+  // set the default tracking algorithm
+  std::string trackingAlg = "KCF";
+
+  // set the tracking algorithm from parameter
+  if(argc>2)
+    trackingAlg = argv[2];
+
+  // create the tracker
+  //! [create]
+  MultiTracker trackers(trackingAlg);
+  //! [create]
+
+  // container of the tracked objects
+  //! [roi]
+  vector<Rect2d> objects;
+  //! [roi]
+
+  // set input video
+  std::string video = argv[1];
+  VideoCapture cap(video);
+
+  Mat frame;
+
+  // get bounding box
+  cap >> frame;
+  //! [selectmulti]
+  selectROI("tracker",frame,objects);
+  //! [selectmulti]
+
+  //quit when the tracked object(s) is not provided
+  if(objects.size()<1)
+    return 0;
+
+  // initialize the tracker
+  //! [init]
+  trackers.add(frame,objects);
+  //! [init]
+
+  // do the tracking
+  printf("Start the tracking process, press ESC to quit.\n");
+  for ( ;; ){
+    // get frame from the video
+    cap >> frame;
+
+    // stop the program if no more images
+    if(frame.rows==0 || frame.cols==0)
+      break;
+
+    //update the tracking result
+    //! [update]
+    trackers.update(frame);
+    //! [update]
+
+    //! [result]
+    // draw the tracked object
+    for(unsigned i=0;i<trackers.objects.size();i++)
+      rectangle( frame, trackers.objects[i], Scalar( 255, 0, 0 ), 2, 1 );
+    //! [result]
+
+    // show image with the tracked object
+    imshow("tracker",frame);
+
+    //quit on ESC button
+    if(waitKey(1)==27)break;
+  }
+
+}
diff --git a/modules/tracking/tutorials/tutorial_customizing_cn_tracker.markdown b/modules/tracking/tutorials/tutorial_customizing_cn_tracker.markdown
new file mode 100644
index 000000000..4d52e2d73
--- /dev/null
+++ b/modules/tracking/tutorials/tutorial_customizing_cn_tracker.markdown
@@ -0,0 +1,73 @@
+Customizing the CN Tracker {#tutorial_customizing_cn_tracker}
+======================
+
+Goal
+----
+
+In this tutorial you will learn how to
+
+-   Set custom parameters for CN tracker.
+-   Use your own feature-extractor function for the CN tracker.
+
+This document contains tutorial for the @ref cv::TrackerKCF.
+
+Source Code
+-----------
+
+@includelineno tracking/samples/tutorial_customizing_cn_tracker.cpp
+
+Explanation
+-----------
+
+This part explains how to set custom parameters and use your own feature-extractor function for the CN tracker.
+If you need a more detailed information to use @ref cv::Tracker, please refer to @ref tutorial_introduction_to_tracker.
+
+-#  **Set Custom Parameters**
+
+    @snippet tracking/samples/tutorial_customizing_cn_tracker.cpp param
+
+    To set custom paramters, an object should be created. Each tracker algorithm has their own parameter format.
+    So, in this case we should use parameter from @ref cv::TrackerKCF since we are interested in modifying the parameter of this tracker algorithm.
+
+    There are several parameters that can be configured as explained in @ref cv::TrackerKCF::Params.
+    For this tutorial, we focussed on the feature extractor functions.
+
+    Several feature types can be used in @ref cv::TrackerKCF.
+    In this case, the grayscale value (1 dimension) and color-names features (10 dimension),
+    will be merged as 11 dimension feature and then compressed into 2 dimension as specified in the code.
+
+    If you want to use another type of pre-defined feature-extractor function, you can check in @ref cv::TrackerKCF::MODE.
+    We will leave the non-compressed feature as 0 since we want to use a customized function.
+
+-#  **Using a custom function**
+
+    You can define your own feature-extractor function for the CN tracker.
+    However, you need to take care about several things:
+    - The extracted feature should have the same size as the size of the given bounding box (width and height).
+      For the number of channels you can check the limitation in @ref cv::Mat.
+    - You can only use features that can be compared using Euclidean distance.
+      Features like local binary pattern (LBP) may not be suitable since it should be compared using Hamming distance.
+
+    Since the size of the extracted feature should be in the same size with the given bounding box,
+    we need to take care whenever the given bounding box is partially out of range.
+    In this case, we can copy part of image contained in the bounding box as shown in the snippet below.
+
+    @snippet tracking/samples/tutorial_customizing_cn_tracker.cpp insideimage
+
+    Whenever the copied image is smaller than the given bounding box,
+    padding should be given to the sides where the bounding box is partially out of frame.
+
+    @snippet tracking/samples/tutorial_customizing_cn_tracker.cpp padding
+
+-#  **Defining the feature**
+
+    In this tutorial, the extracted feature is reponse of the Sobel filter in x and y direction.
+    Those Sobel filter responses are concatenated, resulting a feature with 2 channels.
+
+    @snippet tracking/samples/tutorial_customizing_cn_tracker.cpp sobel
+
+-#  **Post processing**
+
+    Make sure to convert the feature into @ref cv::CV_64F data format and normalize its value with range -0.5 to 0.5
+
+    @snippet tracking/samples/tutorial_customizing_cn_tracker.cpp postprocess
diff --git a/modules/tracking/tutorials/tutorial_introduction_to_tracker.markdown b/modules/tracking/tutorials/tutorial_introduction_to_tracker.markdown
new file mode 100644
index 000000000..500df0c1c
--- /dev/null
+++ b/modules/tracking/tutorials/tutorial_introduction_to_tracker.markdown
@@ -0,0 +1,75 @@
+Introduction to OpenCV Tracker {#tutorial_introduction_to_tracker}
+==============================
+
+Goal
+----
+
+In this tutorial you will learn how to
+
+-   Create a tracker object.
+-   Use the roiSelector function to select a ROI from a given image.
+-   Track a specific region in a given image.
+
+Source Code
+-----------
+
+@include tracking/samples/tutorial_introduction_to_tracker.cpp
+
+Explanation
+-----------
+
+-#  **Set up the input video**
+
+    @snippet tracking/samples/tutorial_introduction_to_tracker.cpp help
+
+    In this tutorial, you can choose between video or list of images for the program input.
+    As written in the help, you should specify the input video as parameter of the program.
+    If you want to use image list as input, the image list should have formatted numbering
+    as shown in help. In the help, it means that the image files are numbered with 4 digits
+    (e.g. the file naming will be 0001.jpg, 0002.jpg, and so on).
+
+    You can find video samples in Itseez/opencv_extra/testdata/cv/tracking
+    <https://github.com/Itseez/opencv_extra/tree/master/testdata/cv/tracking>
+
+-#  **Declares the required variables**
+
+    You need roi to record the bounding box of the tracked object. The value stored in this
+    variable will be updated using the tracker object.
+
+    @snippet tracking/samples/tutorial_introduction_to_tracker.cpp vars
+
+    The frame variable is used to hold the image data from each frame of the input video or images list.
+
+-#  **Creating a tracker object**
+
+    @snippet tracking/samples/tutorial_introduction_to_tracker.cpp create
+
+    There are at least 5 types of tracker algorithms that can be used:
+    + MIL
+    + BOOSTING
+    + MEDIANFLOW
+    + TLD
+    + KCF
+
+    Each tracker algorithm has their own advantages and disadvantages, please refer the documentation of @ref cv::Tracker for more detailed information.
+
+-#  **Select the tracked object**
+
+    @snippet tracking/samples/tutorial_introduction_to_tracker.cpp selectroi
+
+    Using this function, you can select the bounding box of the tracked object using a GUI.
+    With default parameters, the selection is started from the center of the box and a middle cross will be shown.
+    See @ref cv::selectROI for more detailed information.
+
+-#  **Initializing the tracker object**
+
+    @snippet tracking/samples/tutorial_introduction_to_tracker.cpp init
+
+    Tracker algorithm should be initialized with the provided image data as well as the bounding box of the tracked object.
+    Make sure that the bounding box is not valid (size more than zero) to avoid the initialization process failed.
+
+-#  **Update**
+
+    @snippet tracking/samples/tutorial_introduction_to_tracker.cpp update
+
+    This update function will perform the tracking process and pass the result to the roi variable.
diff --git a/modules/tracking/tutorials/tutorial_multitracker.markdown b/modules/tracking/tutorials/tutorial_multitracker.markdown
new file mode 100644
index 000000000..4bc3e3c5c
--- /dev/null
+++ b/modules/tracking/tutorials/tutorial_multitracker.markdown
@@ -0,0 +1,49 @@
+Using MultiTracker {#tutorial_multitracker}
+==================
+
+Goal
+----
+
+In this tutorial you will learn how to
+
+-   Create a MultiTracker object.
+-   Track several objects at once using the MultiTracker object.
+
+Source Code
+-----------
+
+@includelineno tracking/samples/tutorial_multitracker.cpp
+
+Explanation
+-----------
+
+-#  **Create the MultiTracker object**
+
+    @snippet tracking/samples/tutorial_multitracker.cpp create
+
+    You can create the MultiTracker object and use the same tracking algorithm for all tracked object as shown in the snippet.
+    If you want to use different type of tracking algorithm for each tracked object, you should define the tracking algorithm whenever a new object is added to the MultiTracker object.
+
+-#  **Selection of multiple objects**
+
+    @snippet tracking/samples/tutorial_multitracker.cpp selectmulti
+
+    You can use @ref cv::selectROI to select multiple objects with
+    the result stored in a vector of @ref cv::Rect2d as shown in the code.
+    You can also use another kind of selection scheme, please refer to @ref cv::selectROI for detailed information.
+
+-#  **Adding the tracked object to MultiTracker**
+
+    @snippet tracking/samples/tutorial_multitracker.cpp init
+
+    You can add all tracked objects at once to the MultiTracker as shown in the code.
+    In this case, all objects will be tracked using same tracking algorithm as specified in decaration of MultiTracker object.
+    If you want to use different tracker algorithms for each tracked object,
+    You should add the tracked objects one by one and specify their tracking algorithm using the variant of @ref cv::MultiTracker::add.
+    @sa cv::MultiTracker::add( const String& trackerType, const Mat& image, const Rect2d& boundingBox )
+
+-#  **Obtaining the result**
+
+    @snippet tracking/samples/tutorial_multitracker.cpp result
+
+    You can access the result from the public variable @ref cv::MultiTracker::objects provided by the MultiTracker class as shown in the code.