vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

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 response 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 normalize the feature with range -0.5 to 0.5
+
+    @snippet tracking/samples/tutorial_customizing_cn_tracker.cpp postprocess

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 opencv_extra/testdata/cv/tracking
+    <https://github.com/opencv/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 7 types of tracker algorithms that can be used:
+    + MIL
+    + BOOSTING
+    + MEDIANFLOW
+    + TLD
+    + KCF
+    + MOSSE
+
+    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.
+
+-#  **Initializing the tracker object**
+
+    @snippet tracking/samples/tutorial_introduction_to_tracker.cpp init
+
+    Any tracker algorithm should be initialized with the provided image data, and an initial bounding box of the tracked object.
+    Make sure that the bounding box is valid (size more than zero) to avoid failure of the initialization process.
+
+-#  **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.

modules/tracking/tutorials/tutorial_multitracker.markdown

@@ -0,0 +1,48 @@
+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 selectROI to select multiple objects with
+    the result stored in a vector of @ref cv::Rect2d as shown in the code.
+
+-#  **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::legacy::MultiTracker::add.
+    @sa cv::legacy::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::legacy::MultiTracker::objects provided by the MultiTracker class as shown in the code.