How to improve Kalman Filter performance?

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I want to detect and track player on video but when a detected object lost, it get a new tracking ID. I want to get old tracking id with old object. How can I improve it? This code owner tell me " You should config Kalman Filter " . I'm very confuse about it.

public class Kalman extends KalmanFilter {
private KalmanFilter kalman;
private Point LastResult;
private double deltatime;

public void init() {

}

public Kalman(Point pt){
    kalman = new KalmanFilter(4, 2, 0, CvType.CV_32F);

    Mat transitionMatrix = new Mat(4, 4, CvType.CV_32F, new Scalar(0));
    float[] tM = { 
            1, 0, 1, 0, 
            0, 1, 0, 1,
            0, 0, 1, 0,
            0, 0, 0, 1 } ;
    transitionMatrix.put(0,0,tM);

    kalman.set_transitionMatrix(transitionMatrix);

    LastResult = pt;
    Mat statePre = new Mat(4, 1, CvType.CV_32F, new Scalar(0)); // Toa do (x,y), van toc (0,0)
    statePre.put(0, 0, pt.x);
    statePre.put(1, 0, pt.y);
    kalman.set_statePre(statePre);

    kalman.set_measurementMatrix(Mat.eye(2,4, CvType.CV_32F));

    Mat processNoiseCov = Mat.eye(4, 4, CvType.CV_32F);
    processNoiseCov = processNoiseCov.mul(processNoiseCov, 1e-4); //1e-4
    kalman.set_processNoiseCov(processNoiseCov);

    Mat id1 = Mat.eye(2,2, CvType.CV_32F);
    id1 = id1.mul(id1,1e-1);   
    kalman.set_measurementNoiseCov(id1);

    Mat id2 = Mat.eye(4,4, CvType.CV_32F);
    //id2 = id2.mul(id2,0.1);
    kalman.set_errorCovPost(id2);
}

public Kalman(Point pt, double dt, double Accel_noise_mag) {
    kalman = new KalmanFilter(4, 2, 0, CvType.CV_32F);
    deltatime = dt;

    Mat transitionMatrix = new Mat(4, 4, CvType.CV_32F, new Scalar(0));
    float[] tM = { 
            1, 0, 1, 0, 
            0, 1, 0, 1,
            0, 0, 1, 0,
            0, 0, 0, 1 } ;
    transitionMatrix.put(0,0,tM);

    kalman.set_transitionMatrix(transitionMatrix);

    // init
    LastResult = pt;
    Mat statePre = new Mat(4, 1, CvType.CV_32F, new Scalar(0)); // Toa do (x,y), van toc (0,0)
    statePre.put(0, 0, pt.x);
    statePre.put(1, 0, pt.y);
    statePre.put(2, 0, 0);
    statePre.put(3, 0, 0);
    kalman.set_statePre(statePre);

    Mat statePost = new Mat(4, 1, CvType.CV_32F, new Scalar(0));
    statePost.put(0, 0, pt.x);
    statePost.put(1, 0, pt.y);
    statePost.put(2, 0, 0);
    statePost.put(3, 0, 0);
    kalman.set_statePost(statePost);

    kalman.set_measurementMatrix(Mat.eye(2,4, CvType.CV_32F));

    //Mat processNoiseCov = Mat.eye(4, 4, CvType.CV_32F);
    Mat processNoiseCov = new Mat(4, 4, CvType.CV_32F, new Scalar(0));
    float[] dTime = { (float) (Math.pow(deltatime, 4.0) / 4.0), 0,
            (float) (Math.pow(deltatime, 3.0) / 2.0), 0, 0,
            (float) (Math.pow(deltatime, 4.0) / 4.0), 0,
            (float) (Math.pow(deltatime, 3.0) / 2.0),
            (float) (Math.pow(deltatime, 3.0) / 2.0), 0,
            (float) Math.pow(deltatime, 2.0), 0, 0,
            (float) (Math.pow(deltatime, 3.0) / 2.0), 0,
            (float) Math.pow(deltatime, 2.0) };
    processNoiseCov.put(0, 0, dTime);

    processNoiseCov = processNoiseCov.mul(processNoiseCov, Accel_noise_mag); // Accel_noise_mag = 0.5
    kalman.set_processNoiseCov(processNoiseCov);

    Mat id1 = Mat.eye(2,2, CvType.CV_32F);
    id1 = id1.mul(id1,1e-1);
    kalman.set_measurementNoiseCov(id1);

    Mat id2 = Mat.eye(4,4, CvType.CV_32F);
    id2 = id2.mul(id2,.1);
    kalman.set_errorCovPost(id2);
}

public Point getPrediction() {
    Mat prediction ...