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Realtime Social Distance Monitoring using YOLO V3 Algorithm During Covid19

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dc.contributor.author Shovo, Mehedi Hassan
dc.contributor.author Rifat, MD. Imran Hasan
dc.contributor.author Roy, Karobi
dc.date.accessioned 2022-08-11T05:15:55Z
dc.date.available 2022-08-11T05:15:55Z
dc.date.issued 2021-10-30
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8455
dc.description.abstract Covid-19 dissemination can be slowed via social distancing strategies. Social distance is closely followed as a model to break the chain of dissemination. This study offers a method for detecting social distance violations in public settings such as shopping centers, marketplaces, Banks, and clinics. It would be simple to monitor individuals using this model to see if they are maintaining social distance and to notify them if any deviations from the predefined boundaries occur. From an overhead perspective, the purpose of this project is to construct a deep learning platform for forecasting social distance after victimization. The system uses the YOLO v3 object recognition model to detect humans in video or picture sequences. The transfer learning technique is also utilized to increase the model's accuracy. The detection algorithmic rule in this method makes use of a well before algorithmic rule that's linked to a second trained layer that exploits an overhead human knowledge set. The bounding box information is used by the detection algorithm to identify who has been victimized. The bounding box information is used by the detection model to identify humans. The purpose of this project is to develop a victimization deep learning platform. This algorithm can also be used to perform the procedure on real CCTV footage. The model employs deep learning techniques with the distance in between individuals in the frame and the YOLO v3 model trained just on COCO dataset to identify individuals in the video. The system is designed to fit the environment in which it will be used. YOLO model, COCO dataset, Image processing, Deep learning, and so on are examples of index terms. The distance between the individuals is determined using the associate degree object detection algorithmic rule. A Euclidian distance is calculated and compared to the quality distance provided. A green-colored bounding box will represent those with enough distance, while a red-colored bounding box will indicate those with inadequate distance. The complete range of breaches is also displayed in the output stream. This study intends to halt the deadly virus's spread to a considerable extent with minimal human effort and a low chance of disease for local governments. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Social distance--Health aspects en_US
dc.subject Machine learning en_US
dc.title Realtime Social Distance Monitoring using YOLO V3 Algorithm During Covid19 en_US
dc.type Other en_US

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