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- W4386984166 abstract "The use of social distance as a tool throughout the battle with COVID-19 has shown promising results. In recent years, artificial intelligence (AI) and deep learning (DL) have emerged as a powerful resource for dealing with a wide range of practical challenges and delivering impressively positive outcomes. This article delves into the usage of Object Recognition, and Deep Learning to monitor personal and professional interactions between people at a distance. This research aims to aid in the pandemic fight by creating a technology that may be used as a social distance monitoring system. As evidence mounts in favour of social separation as a first-line, non-pharmaceutical intervention in the precautionary measure against the fastest spreading disease COVID-19, researchers feel that its rigorous observance should be encouraged. This proposal analyses live or recorded video to identify and evaluate correlation metric distances among individual people to verify to see if social distancing is preserved in crowded places." @default.
- W4386984166 created "2023-09-24" @default.
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- W4386984166 date "2023-01-01" @default.
- W4386984166 modified "2023-10-18" @default.
- W4386984166 title "SDD: An Efficient Stacked Deep Learning Technique for Social Distance Detection" @default.
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- W4386984166 doi "https://doi.org/10.1007/978-3-031-37164-6_20" @default.
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