Update default filter documentation

docs/README.md

@@ -14,7 +14,7 @@ The class in charge of performing the tracking of the detections produced by the
 - `initialization_delay (optional)`: The argument `initialization_delay` determines by how large the object's hit counter must be in order to be considered as initialized, and get returned to the user as a real object. It must be smaller than `hit_counter_max` or otherwise the object would never be initialized. If set to 0, objects will get returned to the user as soon as they are detected for the first time, which can be problematic as this can result in objects appearing and immediately dissapearing. Defaults to `hit_counter_max / 2`.
 - `pointwise_hit_counter_max (optional)`: Each tracked object keeps track of how often the points it's tracking have been getting matched. Points that are getting matched (`pointwise_hit_counter > 0`) are said to be live, and points which aren't (`pointwise_hit_counter = 0`) are said to not be live. This is used to determine things like which individual points in a tracked object get drawn by [`draw_tracked_objects`](#draw_tracked_objects) and which don't. This argument (`pointwise_hit_counter_max`) defines how large the inertia for each point of a tracker can grow. Defaults to `5`.
 - `detection_threshold (optional)`: Sets the threshold at which the scores of the points in a detection being fed into the tracker must dip below to be ignored by the tracker. Defaults to `0`.
-- `filter_factory (optional)`: This parameter can be used to change what filter the [`TrackedObject`](#trackedobject) instances created by the tracker will use. Defaults to [`FilterPyKalmanFilterFactory()`](#filterpykalmanfilterfactory).
+- `filter_factory (optional)`: This parameter can be used to change what filter the [`TrackedObject`](#trackedobject) instances created by the tracker will use. Defaults to [`OptimizedKalmanFilterFactory()`](#optimizedkalmanfilterfactory).
 - `past_detections_length`: How many past detections to save for each tracked object. Norfair tries to distribute these past detections uniformly through the object's lifetime so they're more representative. Very useful if you want to add metric learning to your model, as you can associate an embedding to each detection and access them in your distance function. Defaults to `4`.
 
 ### Tracker.update