Human Action Recognition Based on Spatial Temporal Adaptive Residual Graph Convolutional Networks with Attention Mechanism

In human action recognition based on human skeleton data, Spatial-Temporal Graph Convolution Networks (ST-GCN) have recently achieved remarkable performances. However, the ST-GCN model based on fixed skeleton graphs only captures the local physical relationships among skeleton points. This may not b...

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Bibliographic Details
Published in:Chinese Control Conference pp. 7622 - 7627
Main Authors: Song, Lu, He, Yi, Yuan, Huaqing, Du, Peng
Format: Conference Proceeding
Language:English
Published: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Subjects:
Accuracy
Action Recognition
Adaptation models
Adaptive Graph
Adaptive systems
Attention Mechanism
Attention mechanisms
Degradation
Graph Convolution
Graph convolutional networks
Image recognition
Residual Network
ISSN:1934-1768
Online Access:Get full text
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Summary:In human action recognition based on human skeleton data, Spatial-Temporal Graph Convolution Networks (ST-GCN) have recently achieved remarkable performances. However, the ST-GCN model based on fixed skeleton graphs only captures the local physical relationships among skeleton points. This may not be suitable for the diversity of action categories. To address this, we propose a spatial-temporal adaptive residual graph convolutional network with an attention mechanism. We enhance the flexibility of the graph structure by introducing the adaptive graph convolution, which can improve the model's generalization performance and apply it to more data samples. Besides, adding residual links into the graph convolutional network of the STGCN facilitates the fusion of information from both local and global features of human skeleton data, concurrently addressing the issue of network degradation. Additionally, we add the attention mechanism into ST-GCN to emphasize useful features selectively and suppress irrelevant ones. We evaluate the proposed approach on two large-scale datasets: NTU-RGB+D and Kinetics. The experimental results show that our approach surpasses some of the more prominent studies, showing higher accuracy and excellent recognition performance.
ISSN:1934-1768
DOI:10.23919/CCC63176.2024.10662672