Spatialoral graph neural network based on node attention

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Date
2022
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Sciendo
Abstract
Recently, the method of using graph neural network based on skeletons for action recognition has become more and more popular, due to the fact that a skeleton can carry very intuitive and rich action information, without being affected by background, light and other factors. The spatialoral graph convolutional neural network (ST-GCN) is a dynamic skeleton model that automatically learns spatialoral model from data, which not only has stronger expression ability, but also has stronger generalisation ability, showing remarkable results on public data sets. However, the ST-GCN network directly learns the information of adjacent nodes (local information), and is insufficient in learning the relations of non-adjacent nodes (global information), such as clapping action that requires learning the related information of non-adjacent nodes. Therefore, this paper proposes an ST-GCN based on node attention (NA-STGCN), so as to solve the problem of insufficient global information in ST-GCN by introducing node attention module to explicitly model the interdependence between global nodes. The experimental results on the NTU-RGB+D set show that the node attention module can effectively improve the accuracy and feature representation ability of the existing algorithms, and obviously improve the recognition effect of the actions that need global information. © 2021 Qiang Li et al., published by Sciendo 2021.
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This article is licensed under Creative Commons License and full text is openly accessible in CUD Digital Repository. The version of the scholarly record of this article is published in Applied Mathematics and Nonlinear Sciences (2022), available online at: https://doi.org/10.2478/amns.2022.1.00005
Keywords
Action recognition, attention mechanism, skeletons, spatialoral graph convolution
Citation
Li, Q., Wan, J., Zhang, W., & Kweh, Q. L. (2022). Spatialoral graph neural network based on node attention. Applied Mathematics and Nonlinear Sciences. https://doi.org/10.2478/amns.2022.1.00005
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https://doi.org/10.2478/amns.2022.1.00005
 http://hdl.handle.net/20.500.12519/654
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Accounting & Finance