A Multimodal Fusion Behaviors Estimation Method for Public Dangerous Monitoring

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...

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Veröffentlicht in:Journal of advanced computational intelligence and intelligent informatics Jg. 28; H. 3; S. 520 - 527
Hauptverfasser: Hou, Renkai, Xu, Xiangyang, Dai, Yaping, Shao, Shuai, Hirota, Kaoru
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Tokyo Fuji Technology Press Co. Ltd 01.05.2024
Schlagworte:
Algorithms
Behavior
Data integration
Datasets
Deep learning
Emotions
Group dynamics
Identification methods
Informatics
Methods
Neural networks
Speech
Time series
ISSN:1343-0130, 1883-8014
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2024.p0520