class_docstr: A single target track with state space `(x, y, a, h)` and associated
  velocities, where `(x, y)` is the center of the bounding box, `a` is the
  aspect ratio and `h` is the height.
  
  Args:
      mean (ndarray): Mean vector of the initial state distribution.
      covariance (ndarray): Covariance matrix of the initial state distribution.
      track_id (int): A unique track identifier.
      n_init (int): Number of consecutive detections before the track is confirmed.
          The track state is set to `Deleted` if a miss occurs within the first
          `n_init` frames.
      max_age (int): The maximum number of consecutive misses before the track
          state is set to `Deleted`.
      feature (Optional[ndarray]): Feature vector of the detection this track
          originates from. If not None, this feature is added to the `features` cache.
  
  Attributes:
      hits (int): Total number of measurement updates.
      age (int): Total number of frames since first occurance.
      time_since_update (int): Total number of frames since last measurement
          update.
      state (TrackState): The current track state.
      features (List[ndarray]): A cache of features. On each measurement update,
          the associated feature vector is added to this list.
code: def __init__(
      self, mean, covariance, track_id, n_init, max_age, feature=None
  ):
      self.mean = mean
      self.covariance = covariance
      self.track_id = track_id
      self.hits = 1
      self.age = 1
      self.time_since_update = 0
  
      self.state = TrackState.Tentative
      self.features = []
      if feature is not None:
          self.features.append(feature)
  
      self._n_init = n_init
      self._max_age = max_age
  
file_path: PaddleHub/modules/video/multiple_object_tracking/fairmot_dla34/modeling/mot/tracker/base_sde_tracker.py
imports: from ppdet.core.workspace import register, serializable
label_desc: Use image node_id 1 to create a new Track object from inherited base classes: object with example: obj = Track(mean, covariance, track_id, n_init, max_age, feature)