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Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

Qingyue Zhao1, Qiaoyu Gu2, Zhijun Gao3,*, Shipian Shao1, Xinyuan Zhang1

1 School of Management, Shenyang Jianzhu University, Shenyang, 110168, China
2 School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China
3 School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, 110168, China

* Corresponding Author: Zhijun Gao. Email: email

(This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)

Computer Modeling in Engineering & Sciences 2023, 137(2), 1773-1788. https://doi.org/10.32604/cmes.2023.027500

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.

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APA Style
Zhao, Q., Gu, Q., Gao, Z., Shao, S., Zhang, X. (2023). Building indoor dangerous behavior recognition based on LSTM-GCN with attention mechanism. Computer Modeling in Engineering & Sciences, 137(2), 1773-1788. https://doi.org/10.32604/cmes.2023.027500
Vancouver Style
Zhao Q, Gu Q, Gao Z, Shao S, Zhang X. Building indoor dangerous behavior recognition based on LSTM-GCN with attention mechanism. Comput Model Eng Sci. 2023;137(2):1773-1788 https://doi.org/10.32604/cmes.2023.027500
IEEE Style
Q. Zhao, Q. Gu, Z. Gao, S. Shao, and X. Zhang "Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism," Comput. Model. Eng. Sci., vol. 137, no. 2, pp. 1773-1788. 2023. https://doi.org/10.32604/cmes.2023.027500



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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