Pandemic Security System for Police using Neural Networks

Abstract

During this global pandemic named COVID-19 where social distancing is playing a vital role in preventing the spread of this virus among the people. Even after a strong ordinance given by the government to perform a complete lockdown, citizens are being reckless and showing up on the streets. The police are patrolling the streets round the clock to avoid this situation by risking their own life. So to bring ease to their work and also to keep them safe, we have implemented a new system where we will be using a CCTV camera as a medium to detect whether a set of people are gathering in a certain place and inform the map coordinates of that place to the police control station. This will prevent a social gathering of more than 5 people in a place and help us to fight this pandemic by safeguarding the life of people and also the police officers risking their life. For detection, we will be using one the famous technique of Convolution Neural Networking named YOLO. Through this method, we will be detecting the object(person). Once the detection is done, through certain mathematical calculations we will detect the distance between an object by keeping one object as a reference object. Once the distance between them is less than the threshold set there will be an emergency message sent to the police officials which will contain the coordinates of the location and they can prevent this kind of gathering without actually patrolling on the streets.