Class cv::KalmanFilter#

Kalman filter class. View details

#include <opencv2/video/tracking.hpp>

Collaboration diagram for cv::KalmanFilter:

cv::KalmanFilter Node1 cv::KalmanFilter + temp2 + temp3 + temp4 + temp5 + transitionMatrix + KalmanFilter() + KalmanFilter() + correct() + init() + predict() Node2 cv::Mat   + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() and 152 more... + diag() + eye() + eye() + getDefaultAllocator() + getStdAllocator() + ones() + ones() + ones() + ones() + setDefaultAllocator() + zeros() + zeros() + zeros() + zeros() # forEach_impl() Node2->Node1 +controlMatrix +errorCovPost +errorCovPre +gain +measurementMatrix +measurementNoiseCov +processNoiseCov +statePost +statePre +temp1 ... Node3 int     Node3->Node2 +cols +dims +dummy +flags +rows Node4 uint8_t     Node4->Node2 +data +dataend +datalimit +datastart Node5 cv::MatAllocator   + MatAllocator() + ~MatAllocator() + allocate() + allocate() + copy() + deallocate() + download() + getBufferPoolController() + map() + unmap() + upload() Node5->Node2 +allocator Node6 UMatData *     Node6->Node2 +u Node7 MatSize     Node7->Node2 +size Node8 MatStep     Node8->Node2 +step

cv::KalmanFilter Node1 cv::KalmanFilter + temp2 + temp3 + temp4 + temp5 + transitionMatrix + KalmanFilter() + KalmanFilter() + correct() + init() + predict() Node2 cv::Mat   + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() and 152 more... + diag() + eye() + eye() + getDefaultAllocator() + getStdAllocator() + ones() + ones() + ones() + ones() + setDefaultAllocator() + zeros() + zeros() + zeros() + zeros() # forEach_impl() Node2->Node1 +controlMatrix +errorCovPost +errorCovPre +gain +measurementMatrix +measurementNoiseCov +processNoiseCov +statePost +statePre +temp1 ... Node3 int     Node3->Node2 +cols +dims +dummy +flags +rows Node4 uint8_t     Node4->Node2 +data +dataend +datalimit +datastart Node5 cv::MatAllocator   + MatAllocator() + ~MatAllocator() + allocate() + allocate() + copy() + deallocate() + download() + getBufferPoolController() + map() + unmap() + upload() Node5->Node2 +allocator Node6 UMatData *     Node6->Node2 +u Node7 MatSize     Node7->Node2 +size Node8 MatStep     Node8->Node2 +step

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)

Examples
samples/python/snippets/kalman.py.

Constructor & Destructor Documentation#

KalmanFilter()#

cv::KalmanFilter::KalmanFilter()

Python:

cv.KalmanFilter() -> <KalmanFilter object>

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

KalmanFilter()#

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)Htinv(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)-Hx’(k))

statePre#

Mat cv::KalmanFilter::statePre

predicted state (x’(k)): x(k)=Ax(k-1)+Bu(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)

Source file#

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