Social Distancing and Crowd Density Distribution System for Public Places and Public Transports Using Computer Vision and NLP

Abstract

The world population is growing day by day but the resources are limited. So, on one hand, we have to fulfill our requirements by using resources in an optimized manner, and on the other hand, we are currently in a pandemic for which social distancing is the key to prevent ourselves from the COVID-19 virus. We all travel by bus, train, metros, and other public transports, and we all have faced the problem of overcrowding in public transport once in our life especially during any festive season when there is a lot of rush among people to go from their workplace to their hometown. The problem of overcrowding is very common in developing countries like India where the population is relatively higher than the number of seats in public transports. For instance, if we look at the Delhi metro in which it is almost impossible to travel during the festive season. However, it's a regular problem, but the crowd becomes more when any festive season approaches. And due to such rush, people tend to just get inside the metro without even searching for a relatively less crowded or vacant seat/compartment, resulting in an unusual pattern of density seen across the metro, somewhere empty and somewhere highly crowded that even no place to stand. And now due to the COVID-19 pandemic, the situation worsens, on one hand, we are bound to travel in-crowd for work and on the other hand, we have to maintain social distancing for COVID prevention. So it becomes almost impossible to travel through such public transport. To solve such problems we are proposing a solution, that is, a system that takes care of the public places/transports by scanning and informing passengers where they can move to find a seat or less crowded. It also helps to ensure social distancing in the times of the COVID-19 pandemic.