Real Time Monitoring of Social Distancing using Transformers

Abstract

The current census has observed, maintaining Social Distance in public places is one of the most significant factor in curbing the spread of the Corona Virus. This makes it essential for the authorities of these public places, governmental or non-governmental, to monitor the proper execution of this protocol. The risks of virus spread can be minimized by avoiding physical contact among people. This notion of monitoring Social Distance is trending and raw in its development. Although there are available solutions to this problem using YOLO Model or Tensolflow Object detection api, the purpose of this project is to provide a deep learning model for social distance tracking. In Deep Learning and Artificial Intelligence, Transformers is a technology that has its traditional application in the Natural Language Processing, thought it’s application in object detection is novel and intuitive. This has techniques that use self-attention to overcome the limitations presented by inductive convolutional biases in an efficient way. Here the individuals that are detected using this model will have a bounding box specific to that person’s dimensions and physical location on the image plane. The centroid of these bounding boxes will act as coordinate point of location and relative euclidean distances between these points will be used as a parameter to differentiate between the followers and violators of the said protocol. A violation threshold is also established to evaluate whether or not the distance value infringes the minimum social distance threshold. In This project we are working with the concepts of Computer Vision and Deep Learning algorithms to handle the task.