Qingyue Zhao1, Qiaoyu Gu2, Zhijun Gao3,*, Shipian Shao1, Xinyuan Zhang1
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500
- 26 June 2023
Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA)
model was proposed aiming at the problem that the existing human skeleton-based action recognition methods
cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series,
and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for
time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence.
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