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Copied docs of Kalman Filter from source code to documentation

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Ilya Lysenkov 2011-06-10 17:20:03 +00:00
parent 289a827aed
commit 1ebdfa4992
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modules/video/doc/motion_analysis_and_object_tracking.rst
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@ -263,44 +263,61 @@ KalmanFilter
------------
.. c:type:: KalmanFilter
Kalman filter class ::
class KalmanFilter
{
public:
KalmanFilter();
KalmanFilter(int dynamParams, int measureParams, int controlParams=0);
void init(int dynamParams, int measureParams, int controlParams=0);
// predicts statePre from statePost
const Mat& predict(const Mat& control=Mat());
// corrects statePre based on the input measurement vector
// and stores the result in statePost.
const Mat& correct(const Mat& measurement);
Mat statePre; // predicted state (x'(k)):
// x(k)=A*x(k-1)+B*u(k)
Mat statePost; // corrected state (x(k)):
// x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Mat transitionMatrix; // state transition matrix (A)
Mat controlMatrix; // control matrix (B)
// (it is not used if there is no control)
Mat measurementMatrix; // measurement matrix (H)
Mat processNoiseCov; // process noise covariance matrix (Q)
Mat measurementNoiseCov;// measurement noise covariance matrix (R)
Mat errorCovPre; // priori error estimate covariance matrix (P'(k)):
// P'(k)=A*P(k-1)*At + Q)*/
Mat gain; // Kalman gain matrix (K(k)):
// K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
Mat errorCovPost; // posteriori error estimate covariance matrix (P(k)):
// P(k)=(I-K(k)*H)*P'(k)
...
};
Kalman filter class.
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.c`` .
. However, you can modify ``transitionMatrix``, ``controlMatrix``, and ``measurementMatrix`` to get an extended Kalman filter functionality. See the OpenCV sample ``kalman.cpp`` .
KalmanFilter::KalmanFilter
--------------------------
.. cpp:function:: KalmanFilter::KalmanFilter()
Creates an empty object that can be initialized later by the function :cpp:func:`KalmanFilter::init`.
.. cpp:function:: KalmanFilter::KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
The full constructor.
:param dynamParams: The dimensionality of the state.
:param measureParams: The dimensionality of the measurement.
:param controlParams: The dimensionality of the control vector.
:param type: Type of the created matrices. Should be ``CV_32F`` or ``CV_64F``.
KalmanFilter::init
------------------
.. cpp:function:: void KalmanFilter::init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
Re-initializes Kalman filter. The previous content is destroyed.
:param dynamParams: The dimensionality of the state.
:param measureParams: The dimensionality of the measurement.
:param controlParams: The dimensionality of the control vector.
:param type: Type of the created matrices. Should be ``CV_32F`` or ``CV_64F``.
KalmanFilter::predict
---------------------
.. cpp:function:: const Mat& KalmanFilter::predict(const Mat& control=Mat())
Computes predicted state
KalmanFilter::correct
---------------------
.. cpp:function:: const Mat& KalmanFilter::correct(const Mat& measurement)
Updates the predicted state from the measurement
BackgroundSubtractor
@ -330,7 +347,7 @@ BackgroundSubtractor::operator()
:param image: The next video frame.
:param fgmask: The foreground mask as 8-bit binary image
:param fgmask: The foreground mask as 8-bit binary image.
BackgroundSubtractor::getBackgroundImage