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:
Mar. 16, 2021

Filed:

Nov. 07, 2017
Applicant:

Hangzhou Hikvision Digital Technology Co., Ltd., Zhejiang, CN;

Inventors:

Ping Yang, Zhejiang, CN;

Shiliang Pu, Zhejiang, CN;

Di Xie, Zhejiang, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00718 (2013.01); G06K 9/00369 (2013.01); G06K 9/00744 (2013.01); G06K 9/6256 (2013.01); G06N 3/08 (2013.01);
Abstract

Embodiments of the present application disclose a target detection method and device, and relate to the technical field of video processing. The method comprises: obtaining an image sequence to be detected from a video to be detected according to an image sequence determining algorithm based on video timing (S), extracting a first CNN feature of the image sequence to be detected based on a pre-trained CNN model, performing feature fusion on the first CNN feature based on a second CNN feature to obtain a first fused CNN feature of the image sequence to be detected (S); inputting the first fused CNN feature into the first-level classifier, and obtaining first candidate target regions of the image sequence to be detected from an output of the first-level classifier (S); determining a first input region of the second-level classifier based on the first candidate target regions (S); obtaining a third CNN feature of the first input region based on the first fused CNN feature (S); inputting the third CNN feature into the second-level classifier, and obtaining a target detection result for the image sequence to be detected based on the output of the second-level classifier (S).


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