class_docstr: This class allows the user to try Norfair without any predictive filter or velocity estimation.
  
  This track only by comparing the position of the previous detections to the ones in the current frame.
  
  The throughput of this class in FPS is similar to the one achieved by the
  [`OptimizedKalmanFilterFactory`](#optimizedkalmanfilterfactory) class, so this class exists only for
  comparative purposes and it is not advised to use it for tracking on a real application.
  
  Parameters
  ----------
  FilterFactory : _type_
      _description_
code: def create_filter(self, initial_detection: np.ndarray):
      num_points = initial_detection.shape[0]
      dim_points = initial_detection.shape[1]
      dim_z = dim_points * num_points  # flattened positions
      dim_x = 2 * dim_z  # We need to accommodate for velocities
  
      no_filter = NoFilter(
          dim_x,
          dim_z,
      )
      no_filter.x[:dim_z] = np.expand_dims(
          initial_detection.flatten(), 0
      ).T
      return no_filter
  
imports: from abc import ABC, abstractmethod
  import numpy
  from filterpy.kalman import KalmanFilter
label_desc: Use image node_id 1 for calling the NoFilterFactory obj's underlying member method code with example usage: obj.create_filter(initial_detection) and returns: no_filter