cv::KalmanFilter Class Reference

Kalman filter class. More...

#include <tracking.hpp>

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

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

Detailed Description

Kalman filter class.

The class implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter, [Welch95] . 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)

Constructor & Destructor Documentation

KalmanFilter() [1/2]

CV_WRAP cv::KalmanFilter::KalmanFilter

(

)

KalmanFilter() [2/2]

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

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()

CV_WRAP const Mat & cv::KalmanFilter::correct

(

const Mat &

measurement

)

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()

CV_WRAP const Mat & cv::KalmanFilter::predict

(

const Mat &

control = Mat()

)

Computes a predicted state.

Parameters

control

The optional input control

Member Data Documentation

controlMatrix

CV_PROP_RW Mat cv::KalmanFilter::controlMatrix

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

errorCovPost

CV_PROP_RW Mat cv::KalmanFilter::errorCovPost

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

errorCovPre

CV_PROP_RW Mat cv::KalmanFilter::errorCovPre

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

gain

CV_PROP_RW Mat cv::KalmanFilter::gain

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

measurementMatrix

CV_PROP_RW Mat cv::KalmanFilter::measurementMatrix

measurement matrix (H)

measurementNoiseCov

CV_PROP_RW Mat cv::KalmanFilter::measurementNoiseCov

measurement noise covariance matrix (R)

processNoiseCov

CV_PROP_RW Mat cv::KalmanFilter::processNoiseCov

process noise covariance matrix (Q)

statePost

CV_PROP_RW Mat cv::KalmanFilter::statePost

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

statePre

CV_PROP_RW 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

CV_PROP_RW Mat cv::KalmanFilter::transitionMatrix

state transition matrix (A)

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

• build/release/_deps/opencv-src/include/opencv4/opencv2/video/tracking.hpp