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10.1016/j.scs.2021.102777 http://scihub22266oqcxt.onion/10.1016/j.scs.2021.102777 33619448!7889035!33619448     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33619448&cmd=llinks): Failed to open stream: HTTP request failed! 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Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic |pdf=|usr=}}{{33619448}} gab.com Text Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic JO: Sustain Cities Soc http://www.kidney.de/pget.php?&tw=1&pmid=33619448 #FOAvip The recent outbreak of the COVID-19 affected millions of people worldwide, yet the rate of infected people is increasing. In order to cope with the global pandemic situation and prevent the spread of the virus, various unprecedented precaution measures are adopted by different countries. One of the crucial practices to prevent the spread of viral infection is social distancing. This paper intends to present a social distance framework based on deep learning architecture as a precautionary step that helps to maintain, monitor, manage, and reduce the physical interaction between individuals in a real-time top view environment. We used Faster-RCNN for human detection in the images. As the human's appearance significantly varies in a top perspective; therefore, the architecture is trained on the top view human data set. Moreover, taking advantage of transfer learning, a new trained layer is fused with a pre-trained architecture. After detection, the pair-wise distance between peoples is estimated in an image using Euclidean distance. The detected bounding box's information is utilized to measure the central point of an individual detected bounding box. A violation threshold is defined that uses distance to pixel information and determines whether two people violate social distance or not. Experiments are conducted using various test images; results demonstrate that the framework effectively monitors the social distance between peoples. The transfer learning technique enhances the overall performance of the framework by achieving an accuracy of 96% with a False Positive Rate of 0.6%. Twit Text FOAVip Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic ????Sustain Cities Soc http://www.kidney.de/pget.php?&tw=1&pmid=33619448 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33619448 #FOAvip English Wikipedia <ref>{{Cite pmid|33619448}}</ref> Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic #MMPMID33619448 Ahmed I; Ahmad M; Jeon G Sustain Cities Soc 2021[Jun]; 69 (ä): 102777 PMID33619448show ga The recent outbreak of the COVID-19 affected millions of people worldwide, yet the rate of infected people is increasing. In order to cope with the global pandemic situation and prevent the spread of the virus, various unprecedented precaution measures are adopted by different countries. One of the crucial practices to prevent the spread of viral infection is social distancing. This paper intends to present a social distance framework based on deep learning architecture as a precautionary step that helps to maintain, monitor, manage, and reduce the physical interaction between individuals in a real-time top view environment. We used Faster-RCNN for human detection in the images. As the human's appearance significantly varies in a top perspective; therefore, the architecture is trained on the top view human data set. Moreover, taking advantage of transfer learning, a new trained layer is fused with a pre-trained architecture. After detection, the pair-wise distance between peoples is estimated in an image using Euclidean distance. The detected bounding box's information is utilized to measure the central point of an individual detected bounding box. A violation threshold is defined that uses distance to pixel information and determines whether two people violate social distance or not. Experiments are conducted using various test images; results demonstrate that the framework effectively monitors the social distance between peoples. The transfer learning technique enhances the overall performance of the framework by achieving an accuracy of 96% with a False Positive Rate of 0.6%. äSocial distance monitoring framework using deep learning architecture (epmc).pdf DeepDyve Pubget Overpricing