• Deepak P.
• Dr. Durgam Kumaraswamy

An efficient method of halting the transmission of the contagious Coronavirus Disease is social distance (SD). 2019 The COVID-19 pandemic has unquestionably stopped all global activity. We inhabited a totally different world a few months ago than we do now. A novel virus has led to a global epidemic and significant casualties. According to information released by the World Health Organization (WHO) in late December 2019, this coronavirus originated in Wuhan, China. In light of this, we suggest an active surveillance system to inform people in an area of interest in order to stop the spread of COVID-19. We have two main contributions to make. We first present a vision-based real-time system that makes use of state-of-the-art deep learning models to detect SD violations and deliver subtle aural and visual feedback. Our second contribution is the development of an unique critical social density value, which we use to show that SD violations may be prevented or reduced to a negligible level provided pedestrian densities are maintained below a certain threshold.

convolutional neural network; social distancing; pedestrian detection, linear regression

"SYSTEM FOR COVID-19 THAT USES VISION TO DETECT CRITICAL DENSITIES AND SOCIAL DISTANCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.261-272, October-2022, Available :http://www.jetir.org/papers/JETIRFU06032.pdf

"SYSTEM FOR COVID-19 THAT USES VISION TO DETECT CRITICAL DENSITIES AND SOCIAL DISTANCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. pp261-272, October-2022, Available at : http://www.jetir.org/papers/JETIRFU06032.pdf