Splits a motion history image into a few parts corresponding to separate independent motions (e.g. left hand, right hand).
:param mhi: Motion history image.
:param segmask: Image where the mask found should be stored, single-channel, 32-bit floating-point.
:param boundingRects: Vector that will contain ROIs of motion connected components.
:param timestamp: Current time in milliseconds or other units.
:param segThresh: Segmentation threshold; recommended to be equal to the interval between motion history "steps" or greater.
The function finds all of the motion segments and marks them in ``segmask`` with individual values (1,2,...). It also computes a vector with ROIs of motion connected components. After that the motion direction for every component can be calculated with :cpp:func:`calcGlobalOrientation` using the extracted mask of the particular component.
.. index:: CamShift
CamShift
...
...
@@ -269,6 +293,9 @@ The class implements a standard Kalman filter
http://en.wikipedia.org/wiki/Kalman_filter
. However, you can modify ``transitionMatrix``, ``controlMatrix``, and ``measurementMatrix`` to get an extended Kalman filter functionality. See the OpenCV sample ``kalman.cpp`` .
.. index:: KalmanFilter::KalmanFilter
KalmanFilter::KalmanFilter
--------------------------
...
...
@@ -289,6 +316,8 @@ KalmanFilter::KalmanFilter
:param type: Type of the created matrices. Should be ``CV_32F`` or ``CV_64F``.
.. index:: KalmanFilter::init
KalmanFilter::init
------------------
...
...
@@ -304,6 +333,9 @@ KalmanFilter::init
:param type: Type of the created matrices. Should be ``CV_32F`` or ``CV_64F``.
.. index:: KalmanFilter::predict
KalmanFilter::predict
---------------------
...
...
@@ -312,6 +344,8 @@ KalmanFilter::predict
Computes predicted state
.. index:: KalmanFilter::correct
KalmanFilter::correct
---------------------
...
...
@@ -320,6 +354,8 @@ KalmanFilter::correct
Updates the predicted state from the measurement
.. index:: BackgroundSubtractor
BackgroundSubtractor
--------------------
...
...
@@ -338,6 +374,9 @@ The base class for background/foreground segmentation. ::
The class is only used to define the common interface for the whole family of background/foreground segmentation algorithms.
The class implements the following algorithm: P. KadewTraKuPong and R. Bowden, An improved adaptive background mixture model for real-time tracking with shadow detection, Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
@@ -416,6 +467,8 @@ The class implements the Gaussian mixture model background subtraction from:
* Z.Zivkovic, F. van der Heijden, Efficient Adaptive Density Estimapion per Image Pixel for the Task of Background Subtraction, Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006. The algorithm similar to the standard Stauffer&Grimson algorithm with additional selection of the number of the Gaussian components based on: Z.Zivkovic, F.van der Heijden, Recursive unsupervised learning of finite mixture models, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.26, no.5, pages 651-656, 2004.
@@ -453,6 +506,8 @@ Also the class has several less important parameters - things you might change b
:param fTau: Shadow threshold. The shadow is detected if the pixel is darker version of the background. Tau is a threshold on how much darker the shadow can be. Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow. See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.