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:
Jan. 31, 2023

Filed:

Dec. 01, 2020
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Shaopeng Tang, Beijing, CN;

Anbang Yao, Beijing, CN;

Yurong Chen, Beijing, CN;

Assignee:

INTEL CORPORATION, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 40/20 (2022.01); G06K 9/62 (2022.01); G06T 7/215 (2017.01); G06V 10/50 (2022.01); G06V 20/52 (2022.01); G06V 20/64 (2022.01); G06T 7/579 (2017.01); G06T 7/593 (2017.01);
U.S. Cl.
CPC ...
G06V 40/28 (2022.01); G06K 9/6269 (2013.01); G06T 7/215 (2017.01); G06T 7/579 (2017.01); G06T 7/596 (2017.01); G06V 10/50 (2022.01); G06V 20/52 (2022.01); G06V 20/64 (2022.01); G06V 40/23 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20132 (2013.01);
Abstract

Techniques are provided for recognition of activity in a sequence of video image frames that include depth information. A methodology embodying the techniques includes segmenting each of the received image frames into a multiple windows and generating spatio-temporal image cells from groupings of windows from a selected sub-sequence of the frames. The method also includes calculating a four dimensional (4D) optical flow vector for each of the pixels of each of the image cells and calculating a three dimensional (3D) angular representation from each of the optical flow vectors. The method further includes generating a classification feature for each of the image cells based on a histogram of the 3D angular representations of the pixels in that image cell. The classification features are then provided to a recognition classifier configured to recognize the type of activity depicted in the video sequence, based on the generated classification features.


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