public void KalmanFilter()
{
mousePoints = new List <PointF>();
kalmanPoints = new List <PointF>();
kal = new KalmanFilter(4, 2, 0);
syntheticData = new SyntheticData();
Matrix <float> state = new Matrix <float>(new float[]
{
0.0f, 0.0f, 0.0f, 0.0f
});
/*
* kal.CorrectedState = state;
* kal.TransitionMatrix = syntheticData.transitionMatrix;
* kal.MeasurementNoiseCovariance = syntheticData.measurementNoise;
* kal.ProcessNoiseCovariance = syntheticData.processNoise;
* kal.ErrorCovariancePost = syntheticData.errorCovariancePost;
* kal.MeasurementMatrix = syntheticData.measurementMatrix;
*/
state.Mat.CopyTo(kal.StatePre);
syntheticData.transitionMatrix.Mat.CopyTo(kal.TransitionMatrix); // kal.TransitionMatrix = syntheticData.transitionMatrix;
syntheticData.measurementNoise.Mat.CopyTo(kal.MeasurementNoiseCov); // kal.MeasurementNoiseCovariance = syntheticData.measurementNoise;
syntheticData.processNoise.Mat.CopyTo(kal.ProcessNoiseCov); // kal.ProcessNoiseCovariance = syntheticData.processNoise;
syntheticData.errorCovariancePost.Mat.CopyTo(kal.ErrorCovPost); // kal.ErrorCovariancePost = syntheticData.errorCovariancePost;
syntheticData.measurementMatrix.Mat.CopyTo(kal.MeasurementMatrix); // kal.MeasurementMatrix = syntheticData.measurementMatrix;
}
public void KalmanFilter()
{
mousePoints = new List<PointF>();
kalmanPoints = new List<PointF>();
kal = new Kalman(4, 2, 0);
syntheticData = new SyntheticData();
Matrix<float> state = new Matrix<float>(new float[]
{
0.0f, 0.0f, 0.0f, 0.0f
});
kal.CorrectedState = state;
kal.TransitionMatrix = syntheticData.transitionMatrix;
kal.MeasurementNoiseCovariance = syntheticData.measurementNoise;
kal.ProcessNoiseCovariance = syntheticData.processNoise;
kal.ErrorCovariancePost = syntheticData.errorCovariancePost;
kal.MeasurementMatrix = syntheticData.measurementMatrix;
}
public KalmanTracker()
{
kalmanPoints = new List <PointF>();
kal = new Kalman(4, 2, 0);
syntheticData = new Kalman_Filter.SyntheticData();
Matrix <float> state = new Matrix <float>(new float[]
{
0.0f, 0.0f, 0.0f, 0.0f
});
kal.CorrectedState = state;
kal.TransitionMatrix = syntheticData.transitionMatrix;
kal.MeasurementNoiseCovariance = syntheticData.measurementNoise;
kal.ProcessNoiseCovariance = syntheticData.processNoise;
kal.ErrorCovariancePost = syntheticData.errorCovariancePost;
kal.MeasurementMatrix = syntheticData.measurementMatrix;
}
