US 12,469,293 B2
Object monitoring system and methods
Jordan Ari Farber, Santa Clara, CA (US); and Nathan John Kopp, Fremont, CA (US)
Assigned to THE CHAMBERLAIN GROUP LLC., Oak Brook, IL (US)
Filed by The Chamberlain Group LLC, Oak Brook, IL (US)
Filed on Jul. 14, 2021, as Appl. No. 17/375,340.
Claims priority of provisional application 63/076,728, filed on Sep. 10, 2020.
Claims priority of provisional application 63/051,446, filed on Jul. 14, 2020.
Prior Publication US 2022/0019810 A1, Jan. 20, 2022
Int. Cl. G06V 20/52 (2022.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06F 18/40 (2023.01); G06N 20/00 (2019.01); G06V 10/40 (2022.01); G07C 9/10 (2020.01); H04N 7/18 (2006.01)
CPC G06V 20/52 (2022.01) [G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/2413 (2023.01); G06F 18/40 (2023.01); G06N 20/00 (2019.01); G06V 10/40 (2022.01); G07C 9/10 (2020.01); H04N 7/183 (2013.01); H04N 7/188 (2013.01); G06V 2201/08 (2022.01)] 33 Claims
OG exemplary drawing
 
1. An object monitoring system for a secured area, the object monitoring system comprising:
an image sensor operable to capture an image of the secured area, the secured area corresponding to an interior of a garage, wherein the image sensor is part of a movable barrier operator configured to raise and lower a movable barrier associated with the garage;
a memory configured to store a machine learning algorithm trained to identify a vehicle in the secured area, the machine learning algorithm including feature maps of training images captured by the image sensor; and
a processor operably coupled to the image sensor and the memory, the processor having a run mode in which the processor calculates a feature descriptor of the image and utilizes the machine learning algorithm and the image of the secured area to determine whether a vehicle is present in the secured area by determining a correlation between the feature descriptor of the image and the feature maps of the training images, wherein the processor determines a confidence of the image corresponding to one of a plurality of conditions, wherein the processor has a retrain mode wherein the processor retrains the machine learning algorithm, wherein the processor exits the run mode and changes to the retrain mode upon the confidence being below a prescribed threshold, and wherein the processor exits the retrain mode and changes to the run mode after completing retraining.