Please use this identifier to cite or link to this item: http://dspace.univ-temouchent.edu.dz/handle/123456789/2177
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCHERRAK, Mohamed el amine-
dc.contributor.authorBenallal, imad eddine-
dc.contributor.authorBENTAIEB, Samia-
dc.date.accessioned2024-02-13T10:16:18Z-
dc.date.available2024-02-13T10:16:18Z-
dc.date.issued2022-
dc.identifier.urihttp://dspace.univ-temouchent.edu.dz/handle/123456789/2177-
dc.description.abstractSARS-CoV-2 is one of the deadliest pandemics the world has ever seen. More than 6 million people have lost their lives to date. It is a pandemic that has developed at a rapid pace and has caused a health crisis. Thus, doctors and scientists have suggested means of protection in particular the wearing of masks and the respect of social distancing. In our work, the face detection technique based on deep learning is used. We propose a new face mask detection model which is realized using a computer vision image classification algorithm based on the neural network of the CNN architecture and the libraries of the Python language. For the measure of distancing, we use the Euclidean distance for the calculation. All these methods have been implemented in the Raspberry Pi 4 and visualized by a camera in real time.en_US
dc.language.isofren_US
dc.subjectVideo surveillance, mask detection, face detection, distancing measurement, Raspberry Pi, CNN.en_US
dc.subjectVidéo surveillance, détection de masque, détection de visage, mesure de distanciation, Raspberry Pi 4, CNNen_US
dc.titleConception et Réalisation d’un Système Intelligent de Surveillance basée sur Raspberry Pi dans le Contexte du Covid-19en_US
dc.typeThesisen_US
Appears in Collections:Electronique

Files in This Item:
File Description SizeFormat 
memoire.pdf1,28 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.