Abstract:
The corona virus COVID-19 pandemic is causing a global health crisis so the
effective protection methods is maintain social distance in public areas
according to the World Health Organization (WHO). The COVID-19
pandemic forced governments across the world to impose lockdowns to
prevent virus transmissions. Reports indicate that maintaining social distance
at work clearly reduces the risk of transmission.
We will use the dataset to build a COVID-19 social distance detector with
computer vision using Python, OpenCV, and Tensor Flow and Yolo. In our
proposed system we will use live video stream and finally in output it shows
the number of violations when someone not maintaining social distance. Our
goal is to identify whether the person on image/video stream is maintaining
social distance or not with the help of computer vision, machine learning and
deep learning.
The proposed method presented in this paper is mainly for crowd surveillance
and security maintenance. This system can be utilized for events, private
property or places that have massive streams of people entering and leaving the
area, which necessitates vigilant tracking and identification of all individuals
within the premises. With deep learning, particularly CNN, the model is able
to train and learn to identify human beings using the database customized.
