GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition

Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body joints. This omits the important implicit connect...

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Veröffentlicht in:	Sensors Jg. 20; H. 12; S. 3499
Hauptverfasser:	Chan, Wensong, Tian, Zhiqiang, Wu, Yang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 21.06.2020 MDPI
Schlagworte:	action recognition Chemical technology Datasets as Topic Deep learning gated convolutional neural networks graph convolutional networks Hands Humans Joints Joints - physiology Methods Neural networks Neural Networks, Computer Skeleton Skeleton - physiology TP1-1185 deep learning gated convolutional neural networks graph convolutional networks action recognition
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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