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• W4387029011 abstract "Disease transmission can occur in a variety of ways, but the most unexpected way is through air transmission. There are very few diseases that can spread through air. In December 2019, the COVID-19 spread across the world. So, this chapter mainly focuses on monitoring the people who maintain social distancing from the video using pre-trained YOLOv4 algorithm. YOLOv4 algorithm is useful to detect multi-objects from the real-time videos. So, using deep learning algorithm, the chance of spreading the virus and air disease will be reduced. In this chapter, YOLOv4 algorithm is utilized that detects multiple humans and calculates the distance between two objects, and if two humans are violating the rules of social distancing as per the government rules, then the alert will be triggered. In addition, predefined YOLOv4 models are compared with proposed techniques on parameters like confidence score with YOLOv3 model for better accuracy." @default.
• W4387029011 created "2023-09-26" @default.
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• W4387029011 date "2023-01-01" @default.
• W4387029011 modified "2023-09-26" @default.
• W4387029011 title "Multi-object Detection: A Social Distancing Monitoring System" @default.
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• W4387029011 doi "https://doi.org/10.1007/978-981-99-3288-7_10" @default.
• W4387029011 hasPublicationYear "2023" @default.
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