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Social Distance Analyzer along with Face Mask Detection using AI and ML
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 20 |
| Year of Publication: 2022 |
| Authors: Shivam Vinod Verma, R.M. Samant, Mahesh Bhausaheb Nagare, Nikhil Yogesh Chapne, Bipin Kiran Patil |
10.5120/ijca2022922212
|
Shivam Vinod Verma, R.M. Samant, Mahesh Bhausaheb Nagare, Nikhil Yogesh Chapne, Bipin Kiran Patil . Social Distance Analyzer along with Face Mask Detection using AI and ML. International Journal of Computer Applications. 184, 20 ( Jul 2022), 7-9. DOI=10.5120/ijca2022922212
Abstract
The COVID-19 virus spreads through the midst groups of people who are in close contact for an extended period. The chances of spreading a virus are higher when a person who is infected with the virus sneezes, coughs, or talks near others. It is very important for us to stay a minimum of 6 feet away from other people even if you or they do not have any symptoms. Social distancing is the best technique to be followed to reduce the spread of the virus. People are informed to avoid contact with other people, thereby supervising the spread of the virus. Artificial Intelligence and Deep Learning have shown good outcomes for some daily life problems. Computer vision and deep learning techniques are used to see social distancing between people in public places. It uses the YOLOv3 object recognition paradigm to categorize. The detection algorithm uses a pre-trained algorithm that is associated with an extra trained layer using an overhead human data set. Euclidean distance is used in the detection of bounding box centroid's pairwise distances of people are determined. Accuracy up to 98% is achieved by the detection model. Coronavirus outbreaks can be solved by social distancing as well as putting on a face mask. Wearing a mask as well as the ensuing social distancing would save large numbers of lives. So, Face Mask Detection would be used efficiently for the purpose.
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