Graph Convolutional Skeleton-based Action Recognition Method for Intelligent Behavior Analysis

Abstract

Smart education is a new education model using modern information technology,and smart behavior analysis is the core component.In the complex classroom scenarios,traditional action recognition algorithms are seriously deficient in accuracy and timeliness.A graph convolutional method based on separation and attention mechanism (DSA-GCN) is proposed to solve the above problems.First,in order to solve the challenge that traditional algorithms are inherently inadequate in aggregating information in the channel domain,multidimensional channel mapping is performed by point-wise convolution,combining the ability of ST-GC to preserve the original spatio-temporal information with the separation ability of depth-separable convolution in spatial and channel feature learning to enhance model feature learning and abstract expressivity.Second,a multi-dimensional fused attention mechanism is used to enhance the model dynamic sensitivity in the spatial convolution domain using self-attention and channel attention mechanisms,and to enhance the key frame discrimination in the temporal convolution domain using temporal and channel attention fusion method.Experiment results show that DSA-GCN achieves better accuracy and effectiveness performance on NTU RGB+D and N-UCLA datasets,and prove the improvement of the ability to aggregate channel information.

Keywords

• action recognition|smart behavior analysis|skeleton-based action classification|graph convolutional neural network|depth-wise separable convolution|attention mechanism