Authors :
Sanjivani Joshi; Piyush Hadge; Sanket Parsewar; Vaibhav Parsewar; Umar Shaikh
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3zoGeoU
DOI :
https://doi.org/10.5281/zenodo.7789382
Abstract :
The Novel Coronavirus outbreak worldwide
has made people to consider different ways to perhaps
protect themselves from infections. Several safety
precautions, including avoiding crowded areas,
maintaining hand hygiene, and avoiding touching eyes,
mouth, or nose. Researchers, mathematicians,
pharmacists, and other professionals have all been
challenged by the pandemic to find solutions to the
pandemic situation. Algorithms and concepts related to
machine learning also find a great place for many
scientists. Among all the preventive measures, social
distancing is one of the most important protective
methods for flattening the COVID-19. An efficient and
cogent social distancing violation analyzer tool would play
a crucial role in detecting humans if they are not
maintaining social distance. The system examines frames
it receives as input to find humans in the visual frame. The
pairwise distance between each recognized individual is
then calculated, and based on the distance value acquired,
the system will issue an alert for those who are not
maintaining the social distance. To determine how well
the system performs, it is tested using a variety of image
input acquisition techniques, including camera, video,
and image. The proposed system achieves a greater
precision for videos, according to experimental findings.
Applications like human tracking, pedestrian recognition,
and vehicle tracking can all be added to the system.
Keywords :
Social Distancing, Pedestrian Detection, Convolutional Neural Network, YOLO, COVID-19, Object Detection
The Novel Coronavirus outbreak worldwide
has made people to consider different ways to perhaps
protect themselves from infections. Several safety
precautions, including avoiding crowded areas,
maintaining hand hygiene, and avoiding touching eyes,
mouth, or nose. Researchers, mathematicians,
pharmacists, and other professionals have all been
challenged by the pandemic to find solutions to the
pandemic situation. Algorithms and concepts related to
machine learning also find a great place for many
scientists. Among all the preventive measures, social
distancing is one of the most important protective
methods for flattening the COVID-19. An efficient and
cogent social distancing violation analyzer tool would play
a crucial role in detecting humans if they are not
maintaining social distance. The system examines frames
it receives as input to find humans in the visual frame. The
pairwise distance between each recognized individual is
then calculated, and based on the distance value acquired,
the system will issue an alert for those who are not
maintaining the social distance. To determine how well
the system performs, it is tested using a variety of image
input acquisition techniques, including camera, video,
and image. The proposed system achieves a greater
precision for videos, according to experimental findings.
Applications like human tracking, pedestrian recognition,
and vehicle tracking can all be added to the system.
Keywords :
Social Distancing, Pedestrian Detection, Convolutional Neural Network, YOLO, COVID-19, Object Detection
