IoT based crowd estimation and stranger recognition in closed public areas

The population has been marginally increasing over the decade, thereby increasing the demand for resources and outgrowth of several diseases. One such significant year, 2020, has revolutionized the globe and put forth a different trend of living. Occasional public gathering around the world has been a sign of importance and inevitable circumstances. Managing the crowd and adhering to the ethics and norms of government is the reason behind effective diversion of resources and appropriate strategy in reducing human density in a particular location. In this year of 2020, every individual of this world is facing a problem in many issues. But the pandemic COVID19 made the situation of the individual even worse. The place where every individual can access without any restriction is public places. But gathering of peoples in the public places will lead to crowd formation. Government has advised not use the public places for certain period of time (lockdown) in order to avoid the crowd formation. This situation could still exist for few more days. Once the lockdown gets over, the crowd in the public places will get increased day by day. Peoples have waited for this situation for a long time, to get chill out, to breathe fresh air present outside, to have a meet up with their friends etc. Hence, the spreading of virus cannot be predicted and it would vanish after the lockdown comes to an end. This project has been proposed to avoid the crowd formation. Our ideology mainly focuses on closed public spaces like shopping complex, temples, sports arena and malls. The existing methodology of people count in public places doesn't possess the ideology of witnessing the count at any place and any instant of time. This project pays attention to this particular ideology of assisting people to choose a better public place based on their desire even before visiting the place with the help of the information provided to them via application. The process is achieved by implementing two IP cameras in the entry point and exit point of the closed public places. The person detected in the camera, is detected and recognized using Haar Cascade algorithm. The count value of people and the recognition of stranger(law breaker) is achieved using this algorithm. The count value and stranger recognition will be directed to the cloud storage as a real time value. Then the mobile webpage developed will access the data from cloud storage and gives the real time head count of people in particular public spaces. The stranger is recognized and notified to the authenticated user via mail. Henceforth, the crime rates in the city will be reduced.