8 Face mask detection and crowd size estimation using IoT platform

Abstract

Due to the rise in coronavirus disease (CoVID-19) cases, it has been a significant concern for governments all over the world to control the spread of the virus. We aim to aid in controlling the spread of the CoVID-19 virus. The face mask detection and crowd counting model with the Internet of things (IoT) platform (Thingspeak) can be deployed in situations where there is a lack of workforce, and additional assistance is required. Using a camera, an area can be remotely monitored, restricting contact between people and the frontline workers, promoting their safety. The research conducted to date about this topic is solely on detecting facemask or doing crowd counting, respectively. We included both face mask detection and crowd counting, connecting both to the IoT platform, enabling us to access the results online. In previous research work, traditional/wired cameras were used. But in our research work, we have used the concept of a Wi-Fi camera, i.e., wireless camera, by which the camera can be positioned in places where the placement of wires is difficult. For face mask detection, we have created two data sets which include images of faces with masks and without a mask, and this data set is used to train the model. For training and testing the model, we have included TensorFlow, Keras and SKLearn libraries. We have included different deep learning layers in training the model like Dense, Dropout and AveragePooling2D. In the test model, python code is connected to a wireless camera, which can capture live video feed and detect whether the person is wearing a mask or not. We have used OpenCV Histogram of oriented gradients (HOG) descriptor in crowd counting, often used to extract features from image data. Using an OpenCV HOG Descriptor, we can count the number of people present in a crowd. Face mask and crowd count is then sent to the IoT platform. Whenever the person is found wearing a mask, a green box appears around the face, and whenever the person is not wearing a mask, a red box appears around the face. When lots of people are detected by a crowd counting model, a green color box enclosing the entire body appears. This study helps in getting face mask detection and crowd size estimation on a single IoT platform, which helps monitor the data with great ease.