Lightweight Behavior Recognition Method Based on ST-GCN
At present,some mainstream behavior recognition methods have some problems,such as large number of model pa-rameters and too complex computation,which are difficult to satisfy both accuracy and timeliness.The paper,based on spatiotempo-ral graph convolutional networks,introduces a self-training attention mechanism to learn the implicit connections of node features in the input,incorporates neck structures and residual structures in the spatial and temporal modules to reduce model parameters and training difficulty,adds channel squeeze and excitation mechanisms in the spatial and temporal modules to learn global features and improve recognition accuracy.A large number of experiments on the behavior recognition dataset NTU-RGB+D 60 show that the accuracy of CS and CV is increased by 2.8%and 3.1%,respectively,while the computational complexity and the number of parame-ters are reduced by 4.7 and 4.4 times.
lightweightbehavior recognitionbonespatiotemporal graph convolutional networkchannel extrusion and in-centive mechanism
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