#include <tracking.hpp>
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CV_WRAP const Mat & | correct (const Mat &measurement) |
| updates the predicted state from the measurement
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void | init (int dynamParams, int measureParams, int controlParams=0, int type=CV_32F) |
| re-initializes Kalman filter. The previous content is destroyed.
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CV_WRAP | KalmanFilter () |
| the default constructor
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CV_WRAP | KalmanFilter (int dynamParams, int measureParams, int controlParams=0, int type=CV_32F) |
| the full constructor taking the dimensionality of the state, of the measurement and of the control vector
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CV_WRAP const Mat & | predict (const Mat &control=Mat()) |
| computes predicted state
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Kalman filter.
The class implements standard Kalman filter {http://en.wikipedia.org/wiki/Kalman_filter}. However, you can modify KalmanFilter::transitionMatrix, KalmanFilter::controlMatrix and KalmanFilter::measurementMatrix to get the extended Kalman filter functionality.
CV_WRAP cv::KalmanFilter::KalmanFilter |
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CV_WRAP cv::KalmanFilter::KalmanFilter |
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int |
dynamParams, |
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int |
measureParams, |
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int |
controlParams = 0 , |
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int |
type = CV_32F |
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the full constructor taking the dimensionality of the state, of the measurement and of the control vector
CV_WRAP const Mat& cv::KalmanFilter::correct |
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const Mat & |
measurement | ) |
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updates the predicted state from the measurement
void cv::KalmanFilter::init |
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int |
dynamParams, |
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int |
measureParams, |
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int |
controlParams = 0 , |
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int |
type = CV_32F |
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) |
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re-initializes Kalman filter. The previous content is destroyed.
Mat cv::KalmanFilter::controlMatrix |
control matrix (B) (not used if there is no control)
Mat cv::KalmanFilter::errorCovPost |
posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
Mat cv::KalmanFilter::errorCovPre |
priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
Mat cv::KalmanFilter::gain |
Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
Mat cv::KalmanFilter::measurementMatrix |
Mat cv::KalmanFilter::measurementNoiseCov |
measurement noise covariance matrix (R)
Mat cv::KalmanFilter::processNoiseCov |
process noise covariance matrix (Q)
Mat cv::KalmanFilter::statePost |
corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Mat cv::KalmanFilter::statePre |
predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
Mat cv::KalmanFilter::temp1 |
Mat cv::KalmanFilter::temp2 |
Mat cv::KalmanFilter::temp3 |
Mat cv::KalmanFilter::temp4 |
Mat cv::KalmanFilter::temp5 |
Mat cv::KalmanFilter::transitionMatrix |
state transition matrix (A)
The documentation for this class was generated from the following file:
- /usr/obj/OpenCV-2.2.0/OpenCV-2.2.0/modules/video/include/opencv2/video/tracking.hpp