A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models
4th International Conference on Computer and Communication Technologies, IC3T 2022
; 606:27-37, 2023.
Article
in English
| Scopus | ID: covidwho-2300778
ABSTRACT
The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Action recognition; Convolutional Neural Networks (CNN); Dataset; Object detection; YOLOv3 algorithm; Convolution; Convolutional neural networks; COVID-19; Deep neural networks; Image segmentation; Object recognition; Security systems; Signal detection; Viruses; Alert systems; Convolutional neural network; Neural network model; Object detection algorithms; Objects detection; Virus spreads
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
4th International Conference on Computer and Communication Technologies, IC3T 2022
Year:
2023
Document Type:
Article
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