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:
Sep. 05, 2023

Filed:

Apr. 21, 2021
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Shixing Chen, Seattle, WA (US);

Xiaohan Nie, Lynnwood, WA (US);

David Jiatian Fan, Seattle, WA (US);

Dongqing Zhang, Kirkland, WA (US);

Vimal Bhat, Redmond, WA (US);

Muhammad Raffay Hamid, Seattle, WA (US);

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/40 (2022.01); G06N 20/00 (2019.01); G06N 5/04 (2023.01); G06F 16/73 (2019.01); G06F 16/78 (2019.01); G11B 27/34 (2006.01); H04N 5/14 (2006.01); G11B 27/036 (2006.01); G06V 10/75 (2022.01); G06F 18/22 (2023.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G06V 20/46 (2022.01); G06F 16/73 (2019.01); G06F 16/78 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06V 10/751 (2022.01); G06V 20/49 (2022.01); G11B 27/036 (2013.01); G11B 27/34 (2013.01); H04N 5/147 (2013.01);
Abstract

Techniques for automatic scene change detection in a video are described. As one example, a computer-implemented method includes extracting features of a query shot and its neighboring shots of a first set of shots without labels with a query model, determining a key shot of the neighboring shots which is most similar to the query shot based at least in part on the features of the query shot and its neighboring shots, extracting features of the key shot with a key model, training the query model into a trained query model based at least in part on a comparison of the features of the query shot and the features of the key shot, extracting features of a second set of shots with labels with the trained query model, and training a temporal model into a trained temporal model based at least in part on the features extracted from the second set of shots and the labels of the second set of shots.


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