2S-AGCN Human Behavior Recognition Based on New Partition Strategy

Compared with behavior recognition based on RGB images and videos, bone behavior recognition is less susceptible to environmental factors and has stronger robustness. Therefore, in recent years, it has attracted extensive attention in the field of human behavior recognition. The information of adjacent nodes is only contained in the adjacency matrix defined in the traditional Graph Convolution Networks (GCNs), and the non-adjacent nodes which contain important potential information of bone posture are ignored. To solve this problem, a new division strategy of skeleton joint based on Two Stream Adaptive Graph Convolutional Network (2S-AGCN) is proposed in this paper, and the division rule is changed from the original three partitions to five partitions. Compared with the original method, the relationship between physical connection and non-physical connection between skeleton joints is strengthened by the new method. Finally, through experimenting on large data set NTU-RGB+D. The classification accuracy of Top-1 on X-Sub and X-View of NTU-RGB+D data set is 88.6% and 95.5% respectively, which is improved by 0.1% and 0.4% compared with the original model. The performance of this method is demonstrated in the experimental results.