TY - GEN
T1 - A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models
AU - Naveen, Kanna
AU - Mudgala, Nagasai
AU - Roy, Rahul
AU - Pavan Kumar, C. S.
AU - Yasin, Mohamed
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Action recognition
KW - Convolutional Neural Networks (CNN)
KW - Dataset
KW - Object detection
KW - YOLOv3 algorithm
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U2 - 10.1007/978-981-19-8563-8_3
DO - 10.1007/978-981-19-8563-8_3
M3 - Conference contribution
AN - SCOPUS:85152522072
SN - 9789811985621
T3 - Lecture Notes in Networks and Systems
SP - 27
EP - 37
BT - Proceedings of 4th International Conference on Computer and Communication Technologies - IC3T 2022
A2 - Reddy, K. Ashoka
A2 - Devi, B. Rama
A2 - George, Boby
A2 - Raju, K. Srujan
A2 - Sellathurai, Mathini
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Computer and Communication Technologies, IC3T 2022
Y2 - 29 July 2022 through 30 July 2022
ER -