class_docstr: A simple Kalman filter for tracking bounding boxes in image space.
  
  The implementation is referred to https://github.com/nwojke/deep_sort.
  
  Args:
      center_only (bool): If True, distance computation is done with
          respect to the bounding box center position only.
          Defaults to False.
      use_nsa (bool): Whether to use the NSA (Noise Scale Adaptive) Kalman
          Filter, which adaptively modulates the noise scale according to
          the quality of detections. More details in
          https://arxiv.org/abs/2202.11983. Defaults to False.
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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).
      bbox_score (float): The confidence score of the bbox.
          Defaults to 0.
  
  Returns:
      (ndarray, ndarray):  Returns the projected mean and covariance
      matrix of the given state estimate.
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code: def project(
      self,
      mean: np.array,
      covariance: np.array,
      bbox_score: float = 0.0,
  ) -> Tuple[np.array, np.array]:
      """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).
          bbox_score (float): The confidence score of the bbox.
              Defaults to 0.
  
      Returns:
          (ndarray, ndarray):  Returns the projected mean and covariance
          matrix of the given state estimate.
      """
      std = [
          self._std_weight_position * mean[3],
          self._std_weight_position * mean[3],
          1e-1,
          self._std_weight_position * mean[3],
      ]
  
      if self.use_nsa:
          std = [(1 - bbox_score) * x for x in std]
  
      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: mmdetection/mmdet/models/task_modules/tracking/kalman_filter.py
imports: from typing import Tuple
  import numpy
  import torch
  from mmdet.registry import TASK_UTILS
label_desc: Use image node_id 4 for calling the KalmanFilter obj's underlying member method code with example usage: obj.project(mean, covariance, bbox_score) and returns: mean,  unknown