Abstract
COVID-19 is a transmissible infection triggered by a coronavirus that has newly detected effect. Most patients infected by COVID-19 are undergoing minor to severe breathing disease and could able to recover without any therapy. Old persons, and others with underlying medical conditions such as cardiovascular, diabetes and so on are further prone to become disabled.
The best measure to avert and slow down the transmission is to be informed about the COVID-19 virus, disease it causes and how it spreads. Protecting yourself and others from the infectious disease by washing hands or using an alcohol based sanitizer frequently and avoid touching face and most importantly, following social distancing in Public Places.
The proposed detection tool was intended to warn users that a video feed will keep a shielded distance from each other. The camera’s video framework was used as an input and YOLOv3 algorithm for object detection is pre-trained. The distance between persons can be measured and a red frame shows any incompatible pair of persons. The result reveals that the method proposed can calculate metrics for social distance in videos among several persons.
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Prasanth Vaidya, S., Srinu, M. (2022). Monitoring Pedestrian Social Distance System for COVID-19. In: Unhelker, B., Pandey, H.M., Raj, G. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 925. Springer, Singapore. https://doi.org/10.1007/978-981-19-4831-2_18
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DOI: https://doi.org/10.1007/978-981-19-4831-2_18
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