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JACIII Vol.28 No.3 pp. 520-527
doi: 10.20965/jaciii.2024.p0520
(2024)

Research Paper:

A Multimodal Fusion Behaviors Estimation Method for Public Dangerous Monitoring

Renkai Hou, Xiangyang Xu, Yaping Dai, Shuai Shao, and Kaoru Hirota

Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

Corresponding author

Received:
March 25, 2023
Accepted:
December 6, 2023
Published:
May 20, 2024
Keywords:
group behavior recognition, speech emotion recognition, multimodal fusion, deep learning
Abstract

At the present stage, the identification of dangerous behaviors in public places mostly relies on manual work, which is subjective and has low identification efficiency. This paper proposes an automatic identification method for dangerous behaviors in public places, which analyzes group behavior and speech emotion through deep learning network and then performs multimodal information fusion. Based on the fusion results, people can judge the emotional atmosphere of the crowd, make early warning, and alarm for possible dangerous behaviors. Experiments show that the algorithm adopted in this paper can accurately identify dangerous behaviors and has great application value.

Cite this article as:
R. Hou, X. Xu, Y. Dai, S. Shao, and K. Hirota, “A Multimodal Fusion Behaviors Estimation Method for Public Dangerous Monitoring,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 520-527, 2024.
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Last updated on Jul. 12, 2024