Multi-algorithm Fusion Behavior Classification Method for Body Bone Information Reconstruction

Aiming at the poor measurement effect of behavior monitoring in real life,a new method of extracting human behavior features is proposed.It not only considers the body point information,but also integrates the environmental attribute information of image.Considering that a large number of existing e...

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Veröffentlicht in:Ji suan ji ke xue Jg. 49; H. 6; S. 269 - 275
Hauptverfasser: Zhao, Xiao-Hu, Ye, Sheng, Li, Xiao
Format: Journal Article
Sprache:Chinesisch
Veröffentlicht: Chongqing Guojia Kexue Jishu Bu 01.06.2022
Editorial office of Computer Science
Schlagworte:
Algorithms
attention mechanism|graph convolutional network|image recognition|action classification|transformer
Classification
Convolution
Evaluation
Feature extraction
Human behavior
Image classification
Image reconstruction
Object recognition
Thermal expansion
ISSN:1002-137X
Online-Zugang:Volltext
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Zusammenfassung:Aiming at the poor measurement effect of behavior monitoring in real life,a new method of extracting human behavior features is proposed.It not only considers the body point information,but also integrates the environmental attribute information of image.Considering that a large number of existing experiments use a variety of complex algorithms for experimental classification on the basis of human body features extraction,it does not take into account the irrationality of only using the body features for algorithm evaluation.Therefore,an image information reconstruction method based on body features is proposed in this paper,which combines the image convolution network of body features,attention mechanism and image recognition method to realize human behavior recognition.The body point information is extracted by Openpose,and then the body points are classified by graph convolution and attention.On the basis of the first classification,the body point expansion coefficient is added to segment the images so as
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1002-137X
DOI:10.11896/jsjkx.210500070