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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 12737921 B1

PDFFull Text
Date of Patent:
Sep. 15, 2026

Filed:

Aug. 22, 2024

Automated recalibration of sensors for monitoring an area of real space

Applicant:

Standard Cognition, Corp., San Francisco, CA (US);

Inventors:

Nagasrikanth Kallakuri, San Francisco, CA (US);

Tushar Dadlani, Pleasanton, CA (US);

Dhananjay Singh, San Francisco, CA (US);

Daniel L. Fischetti, San Francisco, CA (US);

Aniruddha Maru, San Ramon, CA (US);

Assignee:

Standard Cognition, Corp., San Francisco, CA (US);

Attorneys:

Haynes Beffel & Wolfeld LLP

Andrew L. Dunlap

Primary Examiner:

Aaron W Carter

Int. Cl.
CPC ...
G06T 7/80 (2017.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06Q 10/087 (2023.01); G06V 10/40 (2022.01); G06V 10/46 (2022.01); G06V 10/74 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01); G06V 20/64 (2022.01); G06V 40/10 (2022.01);
U.S. Cl.
CPC ...
G06T 7/80 (2017.01); G06F 18/2148 (2023.01); G06F 18/2193 (2023.01); G06F 18/22 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 10/087 (2013.01); G06V 10/40 (2022.01); G06V 10/46 (2022.01); G06V 10/74 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01); G06V 20/653 (2022.01); G06V 40/10 (2022.01); G06T 2207/30196 (2013.01);
Abstract

Automated techniques provide for recalibrating cameras configured for monitoring an area of real space. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. Transformation information between the selected images and the set of calibration images is obtained based on one or more features extracted from the selected images using a trained neural network classifier corresponding to points located at relatively immobile structures. The features extracted from the selected images match features in the set of calibration images. Camera calibrations can be updated when transform information between features matched meets or exceeds a threshold.

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