def initiate(self, measurement):
        """
        Create track from unassociated measurement.

        Args:
            measurement (ndarray): Bounding box coordinates (x, y, a, h) with
                center position (x, y), aspect ratio a, and height h.

        Returns:
            The mean vector (8 dimensional) and covariance matrix (8x8
            dimensional) of the new track. Unobserved velocities are 
            initialized to 0 mean.
        """
        mean_pos = measurement
        mean_vel = np.zeros_like(mean_pos)
        mean = np.r_[mean_pos, mean_vel]

        std = [
            2 * self._std_weight_position * measurement[3],
            2 * self._std_weight_position * measurement[3], 1e-2,
            2 * self._std_weight_position * measurement[3],
            10 * self._std_weight_velocity * measurement[3],
            10 * self._std_weight_velocity * measurement[3], 1e-5,
            10 * self._std_weight_velocity * measurement[3]
        ]
        covariance = np.diag(np.square(std))
        return mean, np.float32(covariance)

        Create track from unassociated measurement.

        Args:
            measurement (ndarray): Bounding box coordinates (x, y, a, h) with
                center position (x, y), aspect ratio a, and height h.

        Returns:
            The mean vector (8 dimensional) and covariance matrix (8x8
            dimensional) of the new track. Unobserved velocities are 
            initialized to 0 mean.
        
ppdet/modeling/mot/motion/kalman_filter.py