A three-dimensional human motion pose recognition algorithm based on graph convolutional networks

In the task of three-dimensional human motion posture recognition, there are problems such as target loss, inaccurate target positioning, and high computational complexity. This article designs a recognition evaluation algorithm to address these issues. Design a LiteHRNet model for extracting skeleton sequences from action videos, and propose a graph convolutional structure that combines residual networks and attention mechanisms. This network can effectively enhance the expression ability of node key features. Introducing second-order velocity information and spatial position information of joint points to improve positioning accuracy. Improve the TCN and Transformer network models to simultaneously extract local and long-term features throughout the entire model, and more accurately model the temporal correlation between nodes in the entire action sequence. The fusion of Transformer networks can reduce the computational complexity of the model while ensuring its accuracy. The experiment shows that the model has good evaluation performance on multiple datasets.