Abstract
With its enormous spread, the continuing COVID-19 corona virus pandemic has become a worldwide tragedy. Population vulnerability rises as a result of fewer antiviral medicines and a scarcity of virus fighters. By minimizing physical contact between people, the danger of viral propagation can be reduced. Previously, the distance between two individuals, as well as the number of people breaching the distance, could be computed with alarm. In the proposed methodology, including the existing features in the previous methodology and additionally, the total number of people present in a given frame, as well as the number of people who were violated,non-violated are tallied. The most crucial step in improving social separation detection is to use proper camera calibration. It will produce better results and allow you to compute actual measurable units instead of pixels It can also figure out how many people are in a certain location. As a result, the suggested system will be useful in identifying, counting, and alerting persons who are breaching the specified distance, as well as estimating the number of people in the frame to manage corona spread.
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Kalaivaani, P.C.D., Abitha Lakshmi, M., Bhuvana Preetha, V., Darshana, R., Dharani, M.K. (2022). Counting Number of People and Social Distance Detection Using Deep Learning. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_3
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DOI: https://doi.org/10.1007/978-3-031-16364-7_3
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