def initiate(self, measurement):
        """Create track from unassociated measurement.
        Parameters
        ----------
        measurement : ndarray
            Bounding box coordinates (x, y, a, h) with center position (x, y),
            aspect ratio a, and height h.
        Returns
        -------
        (ndarray, ndarray)
            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, covariance
Create track from unassociated measurement.
        Parameters
        ----------
        measurement : ndarray
            Bounding box coordinates (x, y, a, h) with center position (x, y),
            aspect ratio a, and height h.
        Returns
        -------
        (ndarray, ndarray)
            Returns the mean vector (8 dimensional) and covariance matrix (8x8
            dimensional) of the new track. Unobserved velocities are initialized
            to 0 mean.
        
trackers/deepsort_tracker/kalman_filter.py