class_docstr: Creates faster Filters than [`FilterPyKalmanFilterFactory`][norfair.filter.FilterPyKalmanFilterFactory].
  
  It allows the user to create Kalman Filter optimized for tracking and set its parameters.
  
  Parameters
  ----------
  R : float, optional
      Multiplier for the sensor measurement noise matrix.
  Q : float, optional
      Multiplier for the process uncertainty.
  pos_variance : float, optional
      Multiplier for the initial covariance matrix estimation, only in the entries that correspond to position (not speed) variables.
  pos_vel_covariance : float, optional
      Multiplier for the initial covariance matrix estimation, only in the entries that correspond to the covariance between position and speed.
  vel_variance : float, optional
      Multiplier for the initial covariance matrix estimation, only in the entries that correspond to velocity (not position) variables.
code: def __init__(
      self,
      R: float = 4.0,
      Q: float = 0.1,
      pos_variance: float = 10,
      pos_vel_covariance: float = 0,
      vel_variance: float = 1,
  ):
      self.R = R
      self.Q = Q
  
      # entrances P matrix of KF
      self.pos_variance = pos_variance
      self.pos_vel_covariance = pos_vel_covariance
      self.vel_variance = vel_variance
  
imports: from abc import ABC, abstractmethod
  import numpy
  from filterpy.kalman import KalmanFilter
label_desc: Use image node_id 1 to create a new OptimizedKalmanFilterFactory object from inherited base classes: FilterFactory with example: obj = OptimizedKalmanFilterFactory(R, Q, pos_variance, pos_vel_covariance, vel_variance)