SSDT: Distance Tracking Model Based on Deep Learning

Abstract

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and population vulnerability increased all over the world due to lack of effective remedial measures. Nowadays vaccines are available; but in India, only 18.8% population has been fully vaccinated till now. Therefore, social distancing is only precautionary norm to avoid the spreading of this deadly virus. The risk of virus spread can be avoided by adhering to this norm. The main objective of this work is to provide a framework for tracking social distancing violations among people. This paper proposes a deep learning platform-based Smart Social Distancing Tracker (SSDT) model which is trained on MOT (Multiple Object Tracking) datasets. The proposed model is a hybrid approach that is a combination of YOLOv4 as object detection model merged with MF-SORT, Kalman Filter and brute force feature matching technique to distinguish people from background and provide a bounding box around these. Further, the results are also compared with another model, namely, Faster- RCNN in terms of FPS (frames per second), mAP(mean Average Precision) and training time over the dataset. The results show that the proposed model provides better and more balanced results. The experiment has been carried out in challenging conditions including, occlusion and under lighting variations with mAP of 97% and a real-time speed of 24 fps. The datasets provide numerous classes and from all the classes of objects, only people class has been used for identifying people in a closet. The ultimate goal of the model is to provide a tracking solution that will be helpful for different authorities to redesigning the layout of public places and reducing the risk. This model is also helpful in computing the distance between two people in an image and the results confirm that the proposed model successfully distinguishes between individuals who walk too close or breach the social distancing norms.