Machine learning based social distance monitoring and crowd control management system

The data that was acquired by the globe Prosperity Affiliation reveals that the worldwide pandemic of the Covid virus has had a substantial impact on the globe and has now caused damage to more than eight million people all over the various regions of the world. Two of the most current health precautions are advised to be done immediately in order to avoid the spread of the contamination. The first is to wear facial coverings, and the second is to adopt safe social isolation practices. Both of these measures are recommended to be implemented immediately. We provide a productive strategy that is focussed on the continuous computerised observation of individuals in order to discriminate face coverings. This approach is based on the combination of TensorFlow and PC vision. The purpose of this action is to create a secure setting that makes a positive contribution to the overall health and happiness of the inhabitants of the community. Python is provided with an interpretation for the purpose of face mask detection as soon as the image processing system comes to the realisation that a breach has occurred. After then, an infrared sensor is kept in place in order to ascertain the total number of individuals present, supposing that the face covering is identified and that the door will be open. Under the premise that the number of people who are permitted to be inside the room is greater than the threshold, the door will not open.