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Spatial–temporal graph neural network based on node attention

Qiang Li

Qiang Li

School of Electronic and Information Engineering, South China University of TechnologyGuangzhou, China
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Li, Qiang
, 
Jun Wan

Jun Wan

Midea Intelligent Life Research Institute, Midea Real Estate Holding LimitedFoshan, China
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Wan, Jun
, 
Wucong Zhang

Wucong Zhang

Midea Intelligent Life Research Institute, Midea Real Estate Holding LimitedFoshan, China
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Zhang, Wucong
 und 
Qian Long Kweh

Qian Long Kweh

Canadian University Dubai
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Kweh, Qian Long
  
08. Apr. 2022
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Online veröffentlicht: 08. Apr. 2022
Seitenbereich: 703 - 712
Eingereicht: 23. Mai 2021
Akzeptiert: 27. Sept. 2021
DOI: https://doi.org/10.2478/amns.2022.1.00005
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© 2021 Qiang Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

(a) Spatial–temporal skeletal graph. (b) Partitioning strategy graph.
(a) Spatial–temporal skeletal graph. (b) Partitioning strategy graph.

Fig. 2

Structure diagram of NA-STGCN. GCN, graph convolutional neural network; NA-STGCN, node attention spatial–temporal graph convolutional neural network.
Structure diagram of NA-STGCN. GCN, graph convolutional neural network; NA-STGCN, node attention spatial–temporal graph convolutional neural network.

Fig. 3

Change curve of loss values. NA-STGCN, node attention spatial–temporal graph convolutional neural network; ST-GCN, spatial–temporal graph convolutional neural network.
Change curve of loss values. NA-STGCN, node attention spatial–temporal graph convolutional neural network; ST-GCN, spatial–temporal graph convolutional neural network.

Fig. 4

Node activation response maps: (a) top left: ST-GCN clapping action; (b) top right: NA-STGCN clapping action; (c) bottom left: ST-GCN brushing action; (d) bottom right: NA-STGCN brushing action. NA-STGCN, node attention spatial–temporal graph convolutional neural network; ST-GCN, spatial–temporal graph convolutional neural network.
Node activation response maps: (a) top left: ST-GCN clapping action; (b) top right: NA-STGCN clapping action; (c) bottom left: ST-GCN brushing action; (d) bottom right: NA-STGCN brushing action. NA-STGCN, node attention spatial–temporal graph convolutional neural network; ST-GCN, spatial–temporal graph convolutional neural network.

Comparison with representative methods (%)_

Model CS CV
Two-Stream 3DCNN [8] 66.8 72.6
TCN [22] 74.3 83.1
Clip + CNN + MTLN [23] 79.6 84.8
VA-LSTM [10] 79.4 87.6
ST-GCN [13] 81.5 88.3
NA-STGCN (ours) 85.8 89.3
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