Abstract : Covid-19(sars-cov-2) has had a major global impact on the daily lives of billions of people living around the world. Social distancing has proven to be an effective measure to hamper the spread of the disease. The system presented is for analyzing social distancing by calculating the distance between people in order to slow down the spread of the virus. This system utilizes input from video frames to figure out the distance between individuals to alleviate the effect of this pandemic. This is done by evaluating a video feed obtained by a surveillance camera. The video is calibrated into bird’s view and fed as an input to the YOLOv3 model which is an already trained object detection model. The YOLOv3 model is trained using the Common Object in Context (COCO). The proposed system was corroborated on a pre-filmed video. The results and outcomes obtained by the system show that evaluation of the distance between multiple individuals and determining if rules are violated or not. If the distance is less than the minimum threshold value, the individuals are represented by a red bounding box, if not then it is represented by a green bounding box. This system can be further developed to detect social distancing in real-time applications.
Keywords : social distancing, pedestrian detection, deep learning, convolutional neural network
| DOI: 10.17148/IJARCCE.2022.114111
COVID-19 (SARS-CoV-2) imekuwa na athari kubwa ya kimataifa kwenye maisha ya kila siku ya mabilioni ya watu wanaoishi ulimwenguni kote. Kuweka umbali kati ya watu ni hatua muhimu ya kuzuia kuenea kwa ugonjwa huo. Mfumo huo unaotolewa ni kwa ajili ya kuchanganua umbali wa kijamii kwa kuhesabu umbali kati ya watu ili kupunguza kuenea kwa virusi. Mfumo huu hutumia pembejeo kutoka kwa fremu za video kugundua umbali kati ya watu ili kupunguza athari za janga hili. Hii ni kufanyika kwa kutathmini video kulishwa kupatikana na kamera ya ufuatiliaji. Video ni calibrated katika ndege mtazamo na kulishwa kama pembejeo kwa mfano YOLO3 ambayo ni tayari mafunzo kitu kugundua mfano. Mfano wa YOLOv3 ni mafunzo kwa kutumia Common Object katika Muktadha (COCO). Mfumo uliopendekezwa ulithibitishwa kwenye video iliyopigwa filamu mapema. Matokeo ya utafiti huu yanaonyesha kuwa kuna uwezekano mkubwa wa kuathiri uhusiano kati ya watu na kuamua kama sheria zinakiuka au la. Kama umbali ni chini ya kiwango cha chini, watu ni kuwakilishwa na rangi nyekundu bounding sanduku, kama si basi ni kuwakilishwa na rangi ya kijani bounding sanduku. Mfumo huu unaweza kuendelezwa zaidi ili kugundua umbali wa kijamii katika maombi ya wakati halisi. "Kama ilivyoelezwa katika makala ya kwanza ya ""Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti wa Utafiti."""