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doc/tutorials/others/meanshift.markdown

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+Meanshift and Camshift {#tutorial_meanshift}
+======================
+
+@tableofcontents
+
+@prev_tutorial{tutorial_background_subtraction}
+@next_tutorial{tutorial_optical_flow}
+
+Goal
+----
+
+In this chapter,
+
+-   We will learn about the Meanshift and Camshift algorithms to track objects in videos.
+
+Meanshift
+---------
+
+The intuition behind the meanshift is simple. Consider you have a set of points. (It can be a pixel
+distribution like histogram backprojection). You are given a small window (may be a circle) and you
+have to move that window to the area of maximum pixel density (or maximum number of points). It is
+illustrated in the simple image given below:
+
+![image](images/meanshift_basics.jpg)
+
+The initial window is shown in blue circle with the name "C1". Its original center is marked in blue
+rectangle, named "C1_o". But if you find the centroid of the points inside that window, you will
+get the point "C1_r" (marked in small blue circle) which is the real centroid of the window. Surely
+they don't match. So move your window such that the circle of the new window matches with the previous
+centroid. Again find the new centroid. Most probably, it won't match. So move it again, and continue
+the iterations such that the center of window and its centroid falls on the same location (or within a
+small desired error). So finally what you obtain is a window with maximum pixel distribution. It is
+marked with a green circle, named "C2". As you can see in the image, it has maximum number of points. The
+whole process is demonstrated on a static image below:
+
+![image](images/meanshift_face.gif)
+
+So we normally pass the histogram backprojected image and initial target location. When the object
+moves, obviously the movement is reflected in the histogram backprojected image. As a result, the meanshift
+algorithm moves our window to the new location with maximum density.
+
+### Meanshift in OpenCV
+
+To use meanshift in OpenCV, first we need to setup the target, find its histogram so that we can
+backproject the target on each frame for calculation of meanshift. We also need to provide an initial
+location of window. For histogram, only Hue is considered here. Also, to avoid false values due to
+low light, low light values are discarded using **cv.inRange()** function.
+
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/video/meanshift/meanshift.cpp)
+
+-   **Code at glance:**
+    @include samples/cpp/tutorial_code/video/meanshift/meanshift.cpp
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/video/meanshift/meanshift.py)
+
+-   **Code at glance:**
+    @include samples/python/tutorial_code/video/meanshift/meanshift.py
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/video/meanshift/MeanshiftDemo.java)
+
+-   **Code at glance:**
+    @include  samples/java/tutorial_code/video/meanshift/MeanshiftDemo.java
+@end_toggle
+
+Three frames in a video I used is given below:
+
+![image](images/meanshift_result.jpg)
+
+Camshift
+--------
+
+Did you closely watch the last result? There is a problem. Our window always has the same size whether
+the car is very far or very close to the camera. That is not good. We need to adapt the window
+size with size and rotation of the target. Once again, the solution came from "OpenCV Labs" and it
+is called CAMshift (Continuously Adaptive Meanshift) published by Gary Bradsky in his paper
+"Computer Vision Face Tracking for Use in a Perceptual User Interface" in 1998 @cite Bradski98 .
+
+It applies meanshift first. Once meanshift converges, it updates the size of the window as,
+\f$s = 2 \times \sqrt{\frac{M_{00}}{256}}\f$. It also calculates the orientation of the best fitting ellipse
+to it. Again it applies the meanshift with new scaled search window and previous window location.
+The process continues until the required accuracy is met.
+
+![image](images/camshift_face.gif)
+
+### Camshift in OpenCV
+
+It is similar to meanshift, but returns a rotated rectangle (that is our result) and box
+parameters (used to be passed as search window in next iteration). See the code below:
+
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/video/meanshift/camshift.cpp)
+
+-   **Code at glance:**
+    @include samples/cpp/tutorial_code/video/meanshift/camshift.cpp
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/video/meanshift/camshift.py)
+
+-   **Code at glance:**
+    @include samples/python/tutorial_code/video/meanshift/camshift.py
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/video/meanshift/CamshiftDemo.java)
+
+-   **Code at glance:**
+    @include  samples/java/tutorial_code/video/meanshift/CamshiftDemo.java
+@end_toggle
+
+Three frames of the result is shown below:
+
+![image](images/camshift_result.jpg)
+
+Additional Resources
+--------------------
+
+-#  French Wikipedia page on [Camshift](http://fr.wikipedia.org/wiki/Camshift). (The two animations
+    are taken from there)
+2.  Bradski, G.R., "Real time face and object tracking as a component of a perceptual user
+    interface," Applications of Computer Vision, 1998. WACV '98. Proceedings., Fourth IEEE Workshop
+    on , vol., no., pp.214,219, 19-21 Oct 1998
+
+Exercises
+---------
+
+-#  OpenCV comes with a Python [sample](https://github.com/opencv/opencv/blob/master/samples/python/camshift.py) for an interactive demo of camshift. Use it, hack it, understand
+    it.