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Personnel association method and device, and graph convolution network training method and device

A convolutional network, human technology, applied in the computer field to achieve the effect of improving accuracy and efficiency

Active Publication Date: 2020-08-18
SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In view of this, the embodiment of the present application provides a personnel association method and its device, a graph convolutional network training method and its device, in order to solve the problem of how to efficiently and accurately determine the personnel association relationship in the prior art

Method used

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  • Personnel association method and device, and graph convolution network training method and device
  • Personnel association method and device, and graph convolution network training method and device
  • Personnel association method and device, and graph convolution network training method and device

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Experimental program
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Embodiment 1

[0043] figure 1 It shows a schematic flowchart of a personnel association method provided by the embodiment of the present application, and the details are as follows:

[0044] In S101, the first node feature matrix and the first adjacency matrix of the target graph data are obtained, the target graph data is graph data constructed according to the face image data of the target person and the peer relationship of the target person.

[0045] In the embodiment of the present application, graph data is data composed of multiple nodes and edges between nodes, and its information is reflected in the characteristics of the nodes and the structure of the graph. The target graph data is graph data constructed according to the face image data of the target person and the peer relationship of the target person, where the target person can be a person who is active within a preset area. Specifically, each target person corresponds to a node in the target graph data, the face image data ...

Embodiment 2

[0067] The embodiment of the present application provides a graph convolutional network training method, the graph convolutional network training method is used to train the graph convolutional network, and the trained graph convolutional network is applied to the personnel association method as described in the first embodiment , can accurately obtain the node embedding feature matrix of the target graph data, and then accurately determine the personnel relationship. figure 2 A schematic flow diagram of the graph convolutional network training method is shown, and the details are as follows:

[0068] In S201, sample image data is acquired.

[0069] In the embodiment of the present application, the sample graph data is graph data used as a data sample for training a graph convolutional network. Specifically, the sample graph data is graph data constructed based on face image data captured within a community or within a city and determined peer relationships of personnel. Du...

Embodiment 3

[0165] Figure 7 It shows a schematic structural diagram of a personnel-associated device provided by the embodiment of the present application. For the convenience of description, only the parts related to the embodiment of the present application are shown:

[0166] The person association device includes: a first acquisition unit 71 , a graph convolution processing unit 72 , and an association relationship determination unit 73 . in:

[0167] The first acquisition unit 71 is configured to acquire the first node feature matrix and the first adjacency matrix of the target graph data, the target graph data is graph data constructed according to the face image data of the target person and the peer relationship of the target person.

[0168] Graph convolution processing unit 72, configured to input the first node feature matrix and the first adjacency matrix into the trained graph convolution network for graph convolution processing to obtain the node embedding feature matrix c...

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Abstract

The invention is suitable for the technical field of computers, and provides a personnel association method and device and a graph convolution network training method and device. The personnel association method comprises the steps of acquiring a first node feature matrix and a first adjacency matrix of target graph data, wherein the target graph data are graph data constructed according to face image data of target personnel and a peer-to-peer relationship of the target personnel; inputting the first node feature matrix and the first adjacent matrix into a trained graph convolution network for graph convolution processing to obtain a node embedding feature matrix corresponding to the target graph data; and embedding the nodes corresponding to the target graph data into a feature matrix toperform target processing, and determining an association relationship of the target personnel. According to the embodiment of the invention, the personnel association relationship can be efficientlyand accurately determined.

Description

technical field [0001] The present application belongs to the field of computer technology, and in particular relates to a person association method and its device, a graph convolutional network training method and its device. Background technique [0002] In the prior art, there is a processing method for determining the relationship between persons based on image data captured by surveillance. However, due to the huge amount of image data captured by surveillance and the large number of people, the accuracy and efficiency of determining the relationship between people is low. Contents of the invention [0003] In view of this, the embodiment of the present application provides a person association method and its device, a graph convolutional network training method and its device, so as to solve the problem of how to efficiently and accurately determine a person's relationship in the prior art. [0004] The first aspect of the embodiment of the present application provi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06F17/16
CPCG06F17/16G06V40/166G06V40/172G06V20/53G06V20/52G06V10/95G06N3/045G06F18/214Y02D10/00
Inventor 余意
Owner SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD
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