def METHOD_NAME(self, mean, covariance, measurement):
    """Run Kalman filter correction step.
    Parameters
    ----------
    mean : ndarray
        The predicted state's mean vector (8 dimensional).
    covariance : ndarray
        The state's covariance matrix (8x8 dimensional).
    measurement : ndarray
        The 4 dimensional measurement vector (x, y, w, h), where (x, y)
        is the center position, w the width, and h the height of the
        bounding box.
    Returns
    -------
    (ndarray, ndarray)
        Returns the measurement-corrected state distribution.
    """
    self.x = mean.T
    self.P = covariance
    std = [
        self._std_weight_position * mean[2],
        self._std_weight_position * mean[3],
        self._std_weight_position * mean[2],
        self._std_weight_position * mean[3],
    ]
    innovation_cov = np.diag(np.square(std))
    super().METHOD_NAME(measurement, R=innovation_cov)
    return self.x.T, self.P