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:
Nov. 15, 2022

Filed:

Oct. 19, 2020
Applicant:

Tencent Technology (Shenzhen) Company Limited, Guangdong, CN;

Inventors:

Haozhi Huang, Shenzhen, CN;

Xinyu Gong, Shenzhen, CN;

Jingmin Luo, Shenzhen, CN;

Xiaolong Zhu, Shenzhen, CN;

Wei Liu, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 40/20 (2022.01); G06T 7/73 (2017.01); H04L 12/46 (2006.01); H04L 61/103 (2022.01); H04L 67/125 (2022.01); H04L 69/325 (2022.01); H04N 19/126 (2014.01); H04N 19/543 (2014.01); H04N 19/55 (2014.01); H04N 19/59 (2014.01); H04N 19/70 (2014.01); H04N 19/87 (2014.01); G06V 10/44 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 40/20 (2022.01); G06T 7/73 (2017.01); H04L 12/4675 (2013.01); H04L 61/103 (2013.01); H04L 67/125 (2013.01); H04L 69/325 (2013.01); H04N 19/126 (2014.11); H04N 19/543 (2014.11); H04N 19/55 (2014.11); H04N 19/59 (2014.11); H04N 19/70 (2014.11); H04N 19/87 (2014.11); G06T 2207/30196 (2013.01);
Abstract

In a multi-person pose recognition method, a to-be-recognized image is obtained, and a circuitous pyramid network is constructed. The circuitous network pyramid includes parallel phases, and each phase includes downsampling network layers, upsampling network layers, and a first residual connection layer to connect the downsampling and upsampling network layers. The phases are interconnected by a second residual connection layer. The circuitous pyramid network is traversed, by extracting a feature map for each phase, and the feature map of the last phase is determined to be the feature map of the to-be-recognized image. Multi-pose recognition is then performed on the to-be-recognized image according to the feature map to obtain a pose recognition result for the to-be-recognized image.


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