Skelton-based Graph Convolution with Residual Combined with Mixed Attention Mechanism for Multi-Person Posture Recognition
The research of multi-person attitude recognition started lately,with low maturity and high complexity,so the network depth is also deepened,the problem of gradient vanishing is also intensified,and the network performance is also attenuated,resulting in the common problems of poor recognition accuracy and low recognition efficiency.To solve these problems,this paper proposes a model of skelton-based graph convolution with residual combined with mixed attention mechanism for multi-person posture recognition.Through the top-down research path,the pre-processing intervention was used to detect multi-body images and select the single body coordinate frame,and the bone key point architecture map was generated.With the residual block,the network structure was improved to suppress the gradient dispersion,and the mixed attention mechanism was loaded to enable and enhance the model.The proposed model is validated on two datasets,MPII and MSCOCO2017,and has stable distribution on the two datasets with small differences.At the same time,the model in this paper is compared with the comprehensive ability of the model recorded in various important literature in this field.In various fine indicators,the model has been improved to a certain extent,with good stability and uniform distribution.The multi-person pose recognition model proposed in this paper reflects the good recognition effect and efficiency based on the cross-data sets,and adds impetus to the study of multi-person gesture recognition.
multi-person posture recognitionresidualmixed attention mechanismskeletal key point diagramgraph convolution
万方数据
维普
