Social Distance Monitoring Framework Using YOLO V5 Deep Architecture
11th International Conference on Recent Trends in Computing, ICRTC 2022
; 600:703-712, 2023.
Article
in English
| Scopus | ID: covidwho-2248548
ABSTRACT
Due to the current outburst and speedy spread of the COVID-19 pandemic, there is a need to comply with social distancing rules by the general public. The public needs to, at minimum, hold a distance of 3 ft or 1 m among one another to follow strict social distancing as instructed by using the World Health Organization for general public safety. Researchers have proposed many solutions based on deep learning to reduce the current pandemic, including COVID-19 screening, diagnosis, social distancing monitoring, etc. This work focuses explicitly on social distancing monitoring by a deep learning approach. Here we employ the YOLOV5 object detection technique upon different images and videos to develop a strategy to assist and put strict social distancing in public. The YOLOV5 algorithm is more robust and has a quicker detection pace than its competitors. The suggested object detection framework shows an average precision rating of 94.75%. Some of the existing analyses suffer to identify humans within a range. A few identification blunders happen because of overlapping video frames or humans taking walks too near each other. This detection mistake is due to the overlapping structures, and human beings are standing too close to each other. This paper focuses on correctly identifying humans by using and overcoming the flaws of frame overlapping. Following the proposed social distancing technique also yields positive results in numerous variable eventualities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
11th International Conference on Recent Trends in Computing, ICRTC 2022
Year:
2023
Document Type:
Article
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