Human body behavior recognition method and system based on graph convolution network

Abstract

The invention discloses a human body behavior recognition method and system based on a graph convolution network, and the method comprises the steps: extracting human body skeleton information from animage containing human body behaviors, obtaining a human body joint point position information sequence, and constructing a topological graph sequence with any length of a human body skeleton; performing feature extraction and topological structure adaptive evolution on the topological graph sequence through a topological learnable graph convolution-based space-time graph convolution network to obtain new node features fused with local space-time features and a topological graph sequence with a new topological structure; performing feature extraction through a graph convolution long-term andshort-term memory neural network; global spatio-temporal features are obtained through global pooling operation; and performing human body behavior recognition based on the global spatial-temporal features through a classifier. The features of a whole graph are directly learned, the weight matrix in graph convolution is expanded to the whole topological graph structure, the relation between any two nodes in the graph is learned, limitation of the topological structure is avoided, and the recognition accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and relates to a human action recognition method and system based on a graph convolutional network, which can be used for action recognition of topological graph sequences. Background technique [0002] Convolutional neural networks have achieved great results in many fields, but they rely on the data representation to have a grid structure. However, the data in many domains is not a grid structure, and the data in these irregular domains usually show a topological graph structure, which makes it difficult for convolutional neural networks to generalize in graph domains. In recent years, some progress has been made in promoting the convolutional structure in the graph domain. Maintaining the shared weight of the convolution between subgraphs with different numbers of adjacent nodes is an important feature of the convolutional network in the field of grid data. In order to make the...

AI Technical Summary

Problems solved by technology

The existing adaptive graph convolution can only learn the topology adaptive relationship between adjacent nodes, and has insufficient ability to learn the relationship between distant nodes.

In addition, due to the limitation of the transfer matrix in graph convolution, there is often a lack of effective modeling of long-term temporal relationships between sequences of topological graphs.

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