Method CreateLinear

src/Tracker/Kalman.cpp:40–86

\brief TKalmanFilter::CreateLinear \param xy0 \param xyv0

/// \param xyv0
///
void TKalmanFilter::CreateLinear(Point_t xy0, Point_t xyv0)
{
    // We don't know acceleration, so, assume it to process noise.
    // But we can guess, the range of acceleration values thich can be achieved by tracked object.
    // Process noise. (standard deviation of acceleration: m/s^2)
    // shows, woh much target can accelerate.

    // 4 state variables, 2 measurements
    m_linearKalman.init(4, 2, 0, El_t);
    // Transition cv::Matrix
    m_linearKalman.transitionMatrix = (cv::Mat_<track_t>(4, 4) <<
                                        1, 0, m_deltaTime, 0,
                                        0, 1, 0, m_deltaTime,
                                        0, 0, 1, 0,
                                        0, 0, 0, 1);

    // init...
    m_lastPointResult = xy0;
    m_linearKalman.statePre.at<track_t>(0) = xy0.x;  // x
    m_linearKalman.statePre.at<track_t>(1) = xy0.y;  // y
    m_linearKalman.statePre.at<track_t>(2) = xyv0.x; // vx
    m_linearKalman.statePre.at<track_t>(3) = xyv0.y; // vy

    m_linearKalman.statePost.at<track_t>(0) = xy0.x;
    m_linearKalman.statePost.at<track_t>(1) = xy0.y;
    m_linearKalman.statePost.at<track_t>(2) = xyv0.x;
    m_linearKalman.statePost.at<track_t>(3) = xyv0.y;

    cv::setIdentity(m_linearKalman.measurementMatrix);

    m_linearKalman.processNoiseCov = (cv::Mat_<track_t>(4, 4) <<
                                       pow(m_deltaTime,4.0)/4.0	,0						,pow(m_deltaTime,3.0)/2.0		,0,
                                       0						,pow(m_deltaTime,4.0)/4.0	,0							,pow(m_deltaTime,3.0)/2.0,
                                       pow(m_deltaTime,3.0)/2.0	,0						,pow(m_deltaTime,2.0)			,0,
                                       0						,pow(m_deltaTime,3.0)/2.0	,0							,pow(m_deltaTime,2.0));


    m_linearKalman.processNoiseCov *= m_accelNoiseMag;

    cv::setIdentity(m_linearKalman.measurementNoiseCov, cv::Scalar::all(0.1));

    cv::setIdentity(m_linearKalman.errorCovPost, cv::Scalar::all(.1));

    m_initialPoints.reserve(MIN_INIT_VALS);

    m_initialized = true;
}

///
/// \brief TKalmanFilter::CreateLinear