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.

Date of Patent:
Oct. 01, 2024

Filed:

Oct. 08, 2021
Applicant:

Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US);

Inventor:

Michael Jones, Belmont, MA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/40 (2022.01); G05B 23/02 (2006.01); G06F 18/20 (2023.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/246 (2017.01); G06V 10/50 (2022.01);
U.S. Cl.
CPC ...
G06V 20/41 (2022.01); G05B 23/0286 (2013.01); G06F 18/2148 (2023.01); G06F 18/22 (2023.01); G06F 18/285 (2023.01); G06T 7/001 (2013.01); G06T 7/11 (2017.01); G06T 7/248 (2017.01); G06V 10/50 (2022.01); G06V 20/49 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30164 (2013.01);
Abstract

A system for detecting an anomaly in a video of a factory automation scene is disclosed. The system may accept the video; accept a set of training feature vectors derived from spatio-temporal regions of a training video, where a spatio-temporal region is associated with one or multiple training feature vectors; partition the video into multiple sequences of video volumes; produce a sequence of binary difference images for each of the video volumes; count occurrences of each of predetermined patterns of pixels in each binary difference image for each of the video volumes to produce an input feature vector including an input motion feature vector defining a temporal variation of counts of the predetermined patterns for each of the video volumes; produce a set of distances based on the produced input feature vectors and the set of training feature vectors; and detect the anomaly based on the produced set of distances.


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