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: Run Kalman filter prediction step.
  
  Args:
      mean (ndarray): The 8 dimensional mean vector of the object state
          at the previous time step.
      covariance (ndarray): The 8x8 dimensional covariance matrix of the
          object state at the previous time step.
  
  Returns:
      The mean vector and covariance matrix of the predicted state. 
      Unobserved velocities are initialized to 0 mean.
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code: def predict(self, mean, covariance):
      """
      Run Kalman filter prediction step.
  
      Args:
          mean (ndarray): The 8 dimensional mean vector of the object state
              at the previous time step.
          covariance (ndarray): The 8x8 dimensional covariance matrix of the
              object state at the previous time step.
  
      Returns:
          The mean vector and covariance matrix of the predicted state.
          Unobserved velocities are initialized to 0 mean.
      """
      std_pos = [
          self._std_weight_position * mean[3],
          self._std_weight_position * mean[3],
          1e-2,
          self._std_weight_position * mean[3],
      ]
      std_vel = [
          self._std_weight_velocity * mean[3],
          self._std_weight_velocity * mean[3],
          1e-5,
          self._std_weight_velocity * mean[3],
      ]
      motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
  
      # mean = np.dot(self._motion_mat, mean)
      mean = np.dot(mean, self._motion_mat.T)
      covariance = (
          np.linalg.multi_dot(
              (self._motion_mat, covariance, self._motion_mat.T)
          )
          + motion_cov
      )
  
      return mean, covariance
  
file_path: PaddleDetection/deploy/pptracking/python/mot/motion/kalman_filter.py
imports: import numpy
  import scipy.linalg
label_desc: Use image node_id 3 for calling the KalmanFilter obj's underlying member method code with example usage: obj.predict(mean, covariance) and returns: mean,  covariance