Smooth point feature trajectories by Kalman Filter in Video Stabilization

Question

Now, I am researching the topic Video Stabilization. I selected good point features (PFs) to track based on Kanade-Lucas-Tomasi (KLT) tracker as a point feature tracker. After extract N PFs from the first image and tracking the PFs in the next image, Point Feature Trajectories (PFTs) are updated by connecting the tracked N PFs to previous PFTs. And continue,

Now I had a set of PFTs. I want to smooth this set of PFTs to create smooth point feature trajectories (SPFTs) by Kalman Filter. But this SPFTs is seemly like as PFTs. I dont know how to adjust the Kalman Filter parameters. Please help me to find out. Thank you in advance.

//Declare Kalman Filter
KalmanFilter KF (4,2,0);
Mat_<float> state (4,1); 
Mat_<float> measurement (2,1);

void init_kalman(double x, double y)
{

KF.statePre.at<float>(0) = x;
KF.statePre.at<float>(1) = y;
KF.statePre.at<float>(2) = 0;
KF.statePre.at<float>(3) = 0;


KF.transitionMatrix = *(Mat_<float>(4,4) << 1,0,1,0,    0,1,0,1,     0,0,1,0,   0,0,0,1);
KF.processNoiseCov = *(Mat_<float>(4,4) << 0.2,0,0.2,0,  0,0.2,0,0.2,  0,0,0.3,0,  0,0,0,0.3);
setIdentity(KF.measurementMatrix);
setIdentity(KF.processNoiseCov,Scalar::all(1e-4));
setIdentity(KF.measurementNoiseCov,Scalar::all(1e-1));
setIdentity(KF.errorCovPost, Scalar::all(.1));  
 }


 Point2f kalman_predict_correct(double x, double y)
 {
Mat prediction = KF.predict();
Point2f predictPt (prediction.at<float>(0), prediction.at<float>(1));   
measurement(0) = x;
measurement(1) = y; 
Mat estimated = KF.correct(measurement);
Point2f statePt (estimated.at<float>(0), estimated.at<float>(1));
return statePt;
  }
// SMOOTH DATA
measurement.setTo(Scalar(0));
for(size_t m = 0; m < new_track_feature.size(); m++)
{
    for(size_t n = 0; n < new_track_feature[0].point_list.size(); n++)
    {
        init_kalman(new_track_feature[m].point_list[n].point.x, new_track_feature[m].point_list[n].point.y);
        Point2f smooth_feature = kalman_predict_correct(new_track_feature[m].point_list[n].point.x, new_track_feature[m].point_list[n].point.y);
        smooth_feature_point.push_back(PointFeature(n,smooth_feature,USE));
    }
    smooth_feature_track.push_back(TrackFeature(m,smooth_feature_point));
}