Skeleton-Based Action Recognition Using Multibranch Adaptive Graph Convolutional Network With Pose Refinement

A multibranch adaptive graph convolutional network is proposed for human action recognition by combining graph convolutional networks (GCNs), adaptive learning, and multibranch feature extraction. Through the adaptive graph convolution module, this method can adaptively change parameters during the training process, thereby enhancing the flexibility of the model. Furthermore, the integration of shallow-level features (skeleton joints), with deep-level features including skeleton information, motion information, and motion difference information allows our model to capture both spatial and temporal dynamics of human actions, leading to a more comprehensive representation of human action features. The introduction of the spatio-temporal attention mechanism enables our model to focus on key frames and skeleton joints. The attitude correction module makes the input data to the network more reasonable and reduces the interference of noise. The inclusion of the adaptive mechanism makes the network no longer limited to the inherent physical connections, and the flexibility of the network is enhanced. The addition of second-order features makes the features of the skeletal data fully exploited. This attention mechanism enhances the discriminative ability of the model and improves its ability to recognize subtle variations and important cues in human actions. Through experiments on benchmark datasets, NTU-RGB-D and Kinetics-400, our method achieves significant improvements in action recognition performance compared with existing approaches. On the Kinetics-400 dataset, we achieved 36.5% and 59.6% recognition rates under the Top-1 and Top-5 evaluation metrics, respectively, which is an improvement of about 1% compared with the state-of-the-art method. On the NTU-RGB-D dataset, we achieved 95.8% and 89.4% recognition rates under the X-view and X-subject modes, respectively, with excellent results. These results validate the effectiveness of the multi-branch adaptive graph convolutional network for human action recognition tasks.