Human body behavior recognition method based on zero sample learning

A sample learning and recognition method technology, applied in the field of human behavior recognition based on zero-sample learning, can solve problems such as incomplete knowledge transfer, inability to adaptively describe nodes in the graph, and inability to use sample features of unknown classes, etc., to improve Effects on classification performance and accuracy
CN111126218AActive Publication Date: 2020-05-08BEIJING UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING UNIV OF TECH
Publication Date
2020-05-08

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Abstract

The invention discloses a human body behavior recognition method based on zero sample learning, which improves the classification performance and accuracy of a trained classifier and promotes the realization of automatic labeling of human body behavior categories. The method comprises the following steps: (1) constructing a knowledge graph based on action classes and action associated objects, anddynamically updating the relationship of the knowledge graph through a graph convolutional network AMGCN based on an attention mechanism so as to better describe the relationship of nodes in the graph; (2) learning a generative adversarial network WGAN-GCC based on gradient penalty and cyclic consistency constraint, so that a learned generator can better generate unknown class features; and (3) combining the graph convolution network and the generative adversarial network into a double-flow deep neural network, so that the trained classifier is more discriminative.
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Description

technical field

[0001] The invention relates to the technical field of computer vision and pattern recognition, in particular to a human behavior recognition method based on zero-sample learning. Background technique

[0002] Human action recognition is an important research topic in the field of machine learning and computer vision, and has been widely used in many research topics, such as human-computer interaction, video surveillance, motion retrieval and sports video analysis. At present, with the rapid development of Internet technology and new social media, as well as the continuous expansion of the application field of human-computer interaction technology, the data in the form of images and videos is increasing at an alarming rate every day, and the complexity of human behavior involved is also increasing. It is improving day by day, and the number of video categories is also increasing. Faced with the explosive growth of massive video data, a very difficult problem...

Claims

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