class_docstr: A simple Kalman filter for tracking bounding boxes in image space.
  
  The 8-dimensional state space
  
      x, y, a, h, vx, vy, va, vh
  
  contains the bounding box center position (x, y), aspect ratio a, height h,
  and their respective velocities.
  
  Object motion follows a constant velocity model. The bounding box location
  (x, y, a, h) is taken as direct observation of the state space (linear
  observation model).
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docstr: Project state distribution to measurement space.
  
  Args
      mean (ndarray): The state's mean vector (8 dimensional array).
      covariance (ndarray): The state's covariance matrix (8x8 dimensional).
  
  Returns:
      The projected mean and covariance matrix of the given state estimate.
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code: def project(self, mean, covariance):
      """
      Project state distribution to measurement space.
  
      Args
          mean (ndarray): The state's mean vector (8 dimensional array).
          covariance (ndarray): The state's covariance matrix (8x8 dimensional).
  
      Returns:
          The projected mean and covariance matrix of the given state estimate.
      """
      std = np.array(
          [
              self._std_weight_position * mean[3],
              self._std_weight_position * mean[3],
              1e-1,
              self._std_weight_position * mean[3],
          ],
          dtype=np.float32,
      )
  
      if use_numba:
          return nb_project(mean, covariance, std, self._update_mat)
  
      innovation_cov = np.diag(np.square(std))
  
      mean = np.dot(self._update_mat, mean)
      covariance = np.linalg.multi_dot(
          (self._update_mat, covariance, self._update_mat.T)
      )
      return mean, covariance + innovation_cov
  
file_path: PaddleDetection/ppdet/modeling/mot/motion/kalman_filter.py
imports: import numpy
  import scipy.linalg
label_desc: Use image node_id 4 for calling the KalmanFilter obj's underlying member method code with example usage: obj.project(mean, covariance) and returns: mean,  unknown, nb_project