cv::KalmanFilter Class Reference

Kalman filter class. More...

#include <opencv2/video/tracking.hpp>

Collaboration diagram for cv::KalmanFilter:

Public Member Functions
	KalmanFilter ()
	KalmanFilter (int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
const Mat &	correct (const Mat &measurement)
	Updates the predicted state from the measurement.
void	init (int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
	Re-initializes Kalman filter. The previous content is destroyed.
const Mat &	predict (const Mat &control=Mat())
	Computes a predicted state.

Public Attributes
Mat	controlMatrix
	control matrix (B) (not used if there is no control)
Mat	errorCovPost
	posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
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	measurementMatrix
	measurement matrix (H)
Mat	measurementNoiseCov
	measurement noise covariance matrix (R)
Mat	processNoiseCov
	process noise covariance matrix (Q)
Mat	statePost
	corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Mat	statePre
	predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
Mat	temp1
Mat	temp2
Mat	temp3
Mat	temp4
Mat	temp5
Mat	transitionMatrix
	state transition matrix (A)

Detailed Description

Kalman filter class.

The class implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter, [298] . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get an extended Kalman filter functionality.

Note

In C API when CvKalman* kalmanFilter structure is not needed anymore, it should be released with cvReleaseKalman(&kalmanFilter)

Examples

samples/cpp/snippets/kalman.cpp.

Constructor & Destructor Documentation

KalmanFilter() [1/2]

cv::KalmanFilter::KalmanFilter

(

)

Python:
	cv.KalmanFilter(		) ->	<KalmanFilter object>
	cv.KalmanFilter(	dynamParams, measureParams[, controlParams[, type]]	) ->	<KalmanFilter object>

KalmanFilter() [2/2]

cv::KalmanFilter::KalmanFilter	(	int	dynamParams,
		int	measureParams,
		int	controlParams = 0,
		int	type = CV_32F )

Python:
	cv.KalmanFilter(		) ->	<KalmanFilter object>
	cv.KalmanFilter(	dynamParams, measureParams[, controlParams[, type]]	) ->	<KalmanFilter object>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

dynamParams	Dimensionality of the state.
measureParams	Dimensionality of the measurement.
controlParams	Dimensionality of the control vector.
type	Type of the created matrices that should be CV_32F or CV_64F.

Member Function Documentation

correct()

const Mat & cv::KalmanFilter::correct

(

const Mat &

measurement

)

Python:
	cv.KalmanFilter.correct(	measurement	) ->	retval

Updates the predicted state from the measurement.

Parameters

measurement

The measured system parameters

init()

void cv::KalmanFilter::init	(	int	dynamParams,
		int	measureParams,
		int	controlParams = 0,
		int	type = CV_32F )

Re-initializes Kalman filter. The previous content is destroyed.

Parameters

dynamParams	Dimensionality of the state.
measureParams	Dimensionality of the measurement.
controlParams	Dimensionality of the control vector.
type	Type of the created matrices that should be CV_32F or CV_64F.

predict()

const Mat & cv::KalmanFilter::predict

(

const Mat &

control = Mat()

)

Python:
	cv.KalmanFilter.predict(	[, control]	) ->	retval

Computes a predicted state.

Parameters

control

The optional input control

Member Data Documentation

controlMatrix

Mat cv::KalmanFilter::controlMatrix

control matrix (B) (not used if there is no control)

errorCovPost

Mat cv::KalmanFilter::errorCovPost

posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)

errorCovPre

Mat cv::KalmanFilter::errorCovPre

priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*‍/

gain

Mat cv::KalmanFilter::gain

Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)

measurementMatrix

Mat cv::KalmanFilter::measurementMatrix

measurement matrix (H)

measurementNoiseCov

Mat cv::KalmanFilter::measurementNoiseCov

measurement noise covariance matrix (R)

processNoiseCov

Mat cv::KalmanFilter::processNoiseCov

process noise covariance matrix (Q)

statePost

Mat cv::KalmanFilter::statePost

corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))

statePre

Mat cv::KalmanFilter::statePre

predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)

temp1

Mat cv::KalmanFilter::temp1

temp2

Mat cv::KalmanFilter::temp2

temp3

Mat cv::KalmanFilter::temp3

temp4

Mat cv::KalmanFilter::temp4

temp5

Mat cv::KalmanFilter::temp5

transitionMatrix

Mat cv::KalmanFilter::transitionMatrix

state transition matrix (A)

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The documentation for this class was generated from the following file:

• opencv2/video/tracking.hpp