Human body behavior recognition method based on multi-scale attention map convolutional network

Abstract

The invention relates to the technical field of human body behavior recognition, in particular to a human body behavior recognition method based on a multi-scale attention graph convolutional network, which comprises the following steps: acquiring a to-be-recognized original 3D skeleton graph sequence; inputting the original 3D skeleton diagram sequence into a pre-trained human body behavior recognition model; firstly, extracting joint information, skeleton information and motion information from the original 3D skeleton diagram sequence through a multi-branch input module to serve as behavior feature data; then, enabling a multi-scale attention graph convolution module to learn correlation of 3D skeleton joint points based on the behavior feature data, and extracting time sequence information of various behaviors in different duration time; and finally, identifying human body behaviors corresponding to the original 3D skeleton graph sequence through a global attention pooling layer; and outputting a corresponding human body behavior recognition result. The human body behavior recognition method can give consideration to the accuracy and efficiency of human body behavior recognition, so that the recognition effect of human body behavior recognition can be ensured.

Description

technical field

[0001] The invention relates to the technical field of human behavior recognition, in particular to a human behavior recognition method based on a multi-scale attention map convolutional network. Background technique

[0002] Human behavior recognition based on video information is a hot issue in the field of computer vision. It mainly uses technologies such as image processing, image analysis and computer vision to detect, classify and track targets in video sequences, and understand and describe behaviors in video information. . Human behavior recognition generally includes two key links: feature extraction and classification recognition. The first step is to construct feature descriptors to express the information of the target behavior in the video, and the second step is to use the feature descriptors to classify the target behavior, and then identify the category of the target behavior. To recognize human behavior, it is first necessary to use feature...

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