original_docstring: 
          Run Kalman filter correction step.
  
          Args:
              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, a, h), where (x, y) is the center position, a the aspect
                  ratio, and h the height of the bounding box.
  
          Returns:
              The measurement-corrected state distribution.
          
docstring: Run Kalman filter correction step.
docstring_tokens: Run Kalman filter correction step .
code: def update(self, mean, covariance, measurement):
          """
          Run Kalman filter correction step.
  
          Args:
              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, a, h), where (x, y) is the center position, a the aspect
                  ratio, and h the height of the bounding box.
  
          Returns:
              The measurement-corrected state distribution.
          """
          projected_mean, projected_cov = self.project(mean, covariance)
  
          chol_factor, lower = scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)
          kalman_gain = scipy.linalg.cho_solve((chol_factor, lower),
                                               np.dot(covariance, self._update_mat.T).T,
                                               check_finite=False).T
          innovation = measurement - projected_mean
  
          new_mean = mean + np.dot(innovation, kalman_gain.T)
          new_covariance = covariance - np.linalg.multi_dot((kalman_gain, projected_cov, kalman_gain.T))
          return new_mean, new_covariance
code_tokens: def update ( self , mean , covariance , measurement ) : projected_mean , projected_cov = self . project ( mean , covariance ) chol_factor , lower = scipy . linalg . cho_factor ( projected_cov , lower = True , check_finite = False ) kalman_gain = scipy . linalg . cho_solve ( ( chol_factor , lower ) , np . dot ( covariance , self . _update_mat . T ) . T , check_finite = False ) . T innovation = measurement - projected_mean new_mean = mean + np . dot ( innovation , kalman_gain . T ) new_covariance = covariance - np . linalg . multi_dot ( ( kalman_gain , projected_cov , kalman_gain . T ) ) return new_mean , new_covariance
short_docstring: Run Kalman filter correction step.
short_docstring_tokens: Run Kalman filter correction step .
comment: """
          Run Kalman filter correction step.
  
          Args:
              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, a, h), where (x, y) is the center position, a the aspect
                  ratio, and h the height of the bounding box.
  
          Returns:
              The measurement-corrected state distribution.
          """