OpenCV  3.4.16
Open Source Computer Vision
Public Member Functions | List of all members
cv::tracking::UnscentedKalmanFilter Class Referenceabstract

The interface for Unscented Kalman filter and Augmented Unscented Kalman filter. More...

#include <opencv2/tracking/kalman_filters.hpp>

Public Member Functions

virtual ~UnscentedKalmanFilter ()
 
virtual Mat correct (InputArray measurement)=0
 
virtual Mat getErrorCov () const =0
 
virtual Mat getMeasurementNoiseCov () const =0
 
virtual Mat getProcessNoiseCov () const =0
 
virtual Mat getState () const =0
 
virtual Mat predict (InputArray control=noArray())=0
 

Detailed Description

The interface for Unscented Kalman filter and Augmented Unscented Kalman filter.

Constructor & Destructor Documentation

◆ ~UnscentedKalmanFilter()

virtual cv::tracking::UnscentedKalmanFilter::~UnscentedKalmanFilter ( )
inlinevirtual

Member Function Documentation

◆ correct()

virtual Mat cv::tracking::UnscentedKalmanFilter::correct ( InputArray  measurement)
pure virtual

The function performs correction step of the algorithm

Parameters
measurement- the current measurement vector,
Returns
the corrected estimate of the state.

◆ getErrorCov()

virtual Mat cv::tracking::UnscentedKalmanFilter::getErrorCov ( ) const
pure virtual
Returns
the error cross-covariance matrix.

◆ getMeasurementNoiseCov()

virtual Mat cv::tracking::UnscentedKalmanFilter::getMeasurementNoiseCov ( ) const
pure virtual
Returns
the measurement noise cross-covariance matrix.

◆ getProcessNoiseCov()

virtual Mat cv::tracking::UnscentedKalmanFilter::getProcessNoiseCov ( ) const
pure virtual
Returns
the process noise cross-covariance matrix.

◆ getState()

virtual Mat cv::tracking::UnscentedKalmanFilter::getState ( ) const
pure virtual
Returns
the current estimate of the state.

◆ predict()

virtual Mat cv::tracking::UnscentedKalmanFilter::predict ( InputArray  control = noArray())
pure virtual

The function performs prediction step of the algorithm

Parameters
control- the current control vector,
Returns
the predicted estimate of the state.

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