Capacity Estimation from Environmental Audio Signals Using Deep Learning
9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022
; 13258 LNCS:114-124, 2022.
Article
in English
| Scopus | ID: covidwho-1899007
ABSTRACT
Estimating the capacity of a room or venue is essential to avoid overcrowding that could compromise people’s safety. Having enough free space to guarantee a minimal safety distance between people is also essential for health reasons, as in the current COVID-19 pandemic. Already existing systems for automatic crowd counting are mostly based on image or video data, and some of them, using deep learning architectures. In this paper, we study the viability of already existing Deep Learning Crowd Counting systems and propose new alternatives based on new network architectures containing convolutional layers, exclusively based on the use of environmental audio signals. The proposed architecture is able to infer the actual capacity with a higher accuracy in comparison to previous proposals. Consequently, conclusions from the accuracy obtained with out approach are drawn and the possible scope of deep learning based crowd counting systems is discussed. © 2022, Springer Nature Switzerland AG.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022
Year:
2022
Document Type:
Article
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