@Article{cmes.2023.027500,
AUTHOR = {Qingyue Zhao, Qiaoyu Gu, Zhijun Gao, Shipian Shao, Xinyuan Zhang},
TITLE = {Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {1773--1788},
URL = {http://www.techscience.com/CMES/v137n2/53371},
ISSN = {1526-1506},
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.
Finally, an attention layer is designed to enhance the features of key bone points, and Softmax is used to classify
and identify dangerous behaviors. The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics
data sets. Experimental results show that the proposed method can effectively identify some dangerous behaviors
in the building, and its accuracy is higher than those of other similar methods.},
DOI = {10.32604/cmes.2023.027500}
}