Social distancing analyzing project present Deep-Learning social distance monitoring framework using pre trained algorithm (YOLOv3) algorithm for human detection and computing their bounding box centroid information. COVID-19 originated from Wuhan, China, has affected many countries worldwide since
Social distancing analyzing
Social distancing analyzing project present Deep-Learning social distance monitoring framework using pre trained algorithm (YOLOv3) algorithm for human detection and computing their bounding box centroid information. COVID-19 originated from Wuhan, China, has affected many countries worldwide since December 2019. This project purposes a Deep –Learning framework of automatic task of monitoring social distance using surveillance video. YOLOv3 model to segregate humans from back ground and deep sort approach to track the identified people with the help of bounding boxes. Deep learning is the subfield of machine learning. Deep learning is based on human brain neural network. It works like human brain. Deep learning is the central technology behind the advance innovation like, voice controller in devices such as, hand- free, smart phones, and many more. It also used to measures the social distance between the pedestrians. For majority machine learning algorithms, it’s difficult to analyze unstructured data, where deep learning becomes useful. This project is therefore mainly based on this advance technique. For measuring the social distance between the humans and detecting their images and videos we will use the IP camera and GPU Card. IP (Internet Protocol Camera) is the type of digital video camera. It receives control data and sends image data via an IP network. IP camera allows for the extreme level zooming. The GPU (Graphics Processing Unit) also called graphic card or video card. It is electronic circuit that accelerates the creation of images videos and animation. It performs fast calculations. It also can say it is an expansion card which generates a feed of output image to display device.
The system uses computer vision and a deep learning model. With the help of computer vision, the distance between each person can be easily calculated. This system can be integrated with IP camera for surveillance of people during pandemics. The YOLOv3 is an object detection model that takes an image or a video as an input and can simultaneously learn and draw bounding box coordinates. YOLO v3 performs multi-label classification with the help of logistic classifiers. WHO (World Health Organization) is prescribed that people should maintain at least 6 feet of distance among each other in order to follow social distancing is an important containment measure and essential to prevent from COVID-19.
Numpy
Matplot
Opencv
Pandas
Sklearn
Anaconda
Python Software
IP Camera
GPU Card
Social distancing measures sufficient to decrease the average contact rate among individuals can reduce peak of infection curve by more than half.
Social distancing aims to decrease or interrupt transmission of COVID-19.
Deep learning is the central technology behind the advance innovation.
Deep learning is being used by cancer researchers to detect cancer cells automatically.
Social distancing analyzing project present Deep-Learning social distance monitoring framework using pre trained algorithm (YOLOv3) algorithm for human detection and computing their bounding box centroid information.YOLOv3 model to segregate humans from back ground and deep sort approach to track the identified people with the help of bounding boxes.For measuring the social distance between the humans and detecting their images and videos we will use the IP camera and GPU Card. IP (Internet Protocol Camera) is the type of digital video camera. It receives control data and sends image data via an IP network. IP camera allows for the extreme level zooming. The GPU (Graphics Processing Unit) also called graphic card or video card. It is electronic circuit that accelerates the creation of images videos and animation.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| IP Camera | Equipment | 1 | 20000 | 20000 |
| GPU | Equipment | 1 | 50000 | 50000 |
| Total in (Rs) | 70000 |
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