融合内在拓扑与多尺度时间特征的骨架动作识别OA北大核心

Graph convolutional networks play a crucial role in skeleton based human action recognition tasks.In order to solve the problems of existing graph convolutional networks ignoring intrinsic relationships,limited time convolution function,and insufficient exploration of potential functional correlations between joints and bones,a skeleton action recog-nition method integrating intrinsic topology and multi-scale time features is proposed.In order to infer the intrinsic topolog-ical relationships of the context,the model utilizes multi-head self-attention mechanism and shared topology to construct an intrinsic topological space graph convolution module.A multi-scale time convolution module is constructed based on complex action sequence analysis,aiming to expand the time convolution structure and capture multi-scale time features.The model builds a bridge for the interaction of joint and bone information,achieving effective transmission and fusion of both information,in order to further explore the functional correlation between them.The proposed method is validated,on the NTU-RGB+D 60 dataset,achieving a recognition accuracy of 91.5%for CS benchmark and 96.9%for CV bench-mark,on the NTU-RGB+D 120 dataset,achieving an accuracy of 89.0%for C-Sub benchmark and 90.8%for C-Set benchmark,respectively.The experimental results show that the proposed method can more effectively extract skeleton spatio-temporal features and improve recognition accuracy.