Crowd Analysis and Mask Detection using Raspberry Pi-3
Pratiksha Sonar, Bhushan Rokade, H. T. Ingale, Aditya Patil · International Journal of Innovations in Engineering and Science · 2021
Now a days all world suffering from a pandemic issue of COVID-19 to control these situation and to maintain the safety of ourselves we all have to take care of the two things like do not make a crowd and wear the mask properly.And to achieve this requirement of safety this paper works with the help of fog node and camera.Managing the crowd requires an intelligent monitoring technology.In this project, we propose a method to manage the crowd by counting multiple humans in the scene by head detection.In our study, we develop a system using Raspberry Pi 3 board that detects the human heads and provide a count of humans in the region using Open CV-Python.A Haar cascade classifier is trained for human head detection.This work also proposes a fog computing-based face mask detection system for controlling the entry of a person into a facility.The proposed system uses fog nodes to process the video streams captured at various entrances into a facility.Haar-cascade-classifiers are used to detect face portions in the video frames.Each fog node deploys two Mobile Net models, where the first model deals with the dichotomy between mask and no mask case.The second model deals with the dichotomy between proper mask wear and improper mask wear case and is applied only if the first model detects mask in the facial image.This two-level classification allows the entry of people into a facility, only if they wear the mask properly The results of the analysis will be helpful in managing the crowd and mask detection in the area with the help of camera.