Social relationship recognition method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of image relation recognition, and provides a social relation recognition method and device, electronic equipment and a storage medium. The method includes obtaining a to-be-identified image; extracting first human body key point information of a first target person and second human body key point information of a second target person in the to-be-identified image; according to the first human body key point information and the second human body key point information, building key point graph data and skeleton graph data between the first target person and the second target person respectively; extracting key point interaction features of the key point graph data through a preset first graph convolution model, and extracting skeleton interaction features of the skeleton graph data through a preset second graph convolution model; and fusing and classifying the key point interaction features and the skeleton interaction features to obtain a social relationship between the first target person and the second target person. According to the invention, the social relation identification accuracy can be improved.

Description

technical field [0001] The present invention relates to the technical field of image relationship recognition, in particular to a social relationship recognition method, device, electronic equipment and storage medium. Background technique [0002] Social relation (SR) is the foundation of human society, so mastering the social relation of the whole society is extremely important for both national governance and scientific research. Existing methods are mainly based on some independent feature modules to classify, such as facial expressions, the relationship between people and objects, body relationship and background information, etc., these technologies are linked to the data set. [0003] In the PIPA and PISC datasets, background information is often used to judge the relationship type, but in the monitoring scenario, the background information is not very important from the data point of view, and the important thing is the interaction between the limbs. The existing te...

AI Technical Summary

Problems solved by technology

However, this models keypoints and is not optimal for representations of relation types

And the key points are fixed as graph data, so the graph convolution of each layer uses the same graph data, which is not conducive to learning different semantic information at each layer, and has a great impact on the accuracy of social relationship recognition

It can be seen that in the existing technology, there is a problem of low accuracy of social relationship recognition

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