Bone Dual-Stream Attention Enhancement Graph Convolving Human Posture Recognition
In order to solve the lack of value analysis of motion correlation information in the loss of meaning of skeletal nodes and dependency information,the paper proposes a model of bone dual-stream attention enhancement graph convolving human posture recognition.The airspace connection matrix and time domain information matrix between bone joints are constructed on the basis of extracting bone feature nodes.With this basis,dual-flow bone node information processing is performed.Taking advantage of the channel attention mechanism for context processing,decturing key node dependencies and global bone motion implications,a two-domain bone topology weighted by neighborhood nodes is constructed.The comparative validation on two datasets Kinetics and NTU RGB+D shows that the model performs better on different datasets.Horizontal comparison with the more representative mainstream methods in the field is shown,the model outperforms the other models in the recognition accuracy of the nine selected behavioral poses.This method reflects the better recognition rate and stability in human posture recognition,and proves the mining value of spatial-temporal dual-domain bone feature information.
posture recognitiontime and space double domainattention mechanismfigure convolutionskeletal featuresmovement information representation
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