Crowd Analysis for Covid
Sayyed Husain Saddique, Mohammed Ajmal Roshan, S Sarathchandran, Najfal Nezar · Zenodo (CERN European Organization for Nuclear Research) · 2021
In the recent Covid-19 state of affairs, we need to spot the most huddled place and stop the mass unfold of the virus. As countries around the world begin to ease their restrictions, places as various as public areas and high streets, transport hubs, events and attractions, yet as individual offices, face the challenge of reopening safely. This presents several challenges once considering the come back to the 'new normal' and reopening society. Our project provides period of timely crowd analytics with object detection of individuals and average interframe and intraframe metrics, like group size, velocity, placement heatmap, and distancing for a given fundamental measure. We have got to analyze hours of video so as to stream to match the period of time current analytics to historical information for finding events like group of individuals or quick movement. The project examines the character of the crowd and its dynamics with specific relation to the problems of crowd safety.