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  • 标题:Social Distance Monitoring In Video Datasets Using Deep Learning Techniques
  • 本地全文:下载
  • 作者:R.Abinaya B.E.M.E. ; A.Meenatchi ; N.Kavikkarasi
  • 期刊名称:International Journal of Advances in Engineering and Management
  • 电子版ISSN:2395-5252
  • 出版年度:2021
  • 卷号:3
  • 期号:5
  • 页码:870-874
  • DOI:10.35629/5252-0305703740
  • 语种:English
  • 出版社:IJAEM JOURNAL
  • 摘要:To limit the spread of an infectious disease, for instance, Covid-19 is to practice social distancing. This is not a new concept, as most societies have been aware of the value of keeping away from people who are suffering from an infection for many generations. The objective is to reduce transmission, delaying the epidemic peak, reducing the size of the epidemic peak, and spreading cases over a longer time to relieve pressure on the healthcare system. It is an action taken to minimize contact with other individuals. In the fight against the COVID-19, social distancing has proven to be a very effective measure to slow down the spread of the disease. People are asked to limit their interactions with each other, reducing the chances of the virus being spread with physical or close contact. The World Health Organization (WHO) states that “COVID-19 is transmitted via droplets and fomites during close unprotected contact between an infector and infected. A fomite is an object or material which is likely to carry infection, such as clothes, utensils, and furniture. Therefore, transmission of the infection can be avoided by staying away from other people as well as from touching infected fomites. In past also AI/Deep Learning has shown promising results on a number of daily life problems.
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