## 1 Introduction
The performance is compared for each of the deep learning models Faster RCNN, SSD, and YOLO. Reference [27] utilizes the YOLOv4 model for people detection in low light instances to enforce social distancing measures. In [28], a spatio-temporal trajectory-based social distancing measurement and analysis method is proposed. This problem has been further examined in [29, 30, 31]. While various solutions proposed in the literature strive to assess the adherence to social distancing protocols, they fall short of incorporating factors such as mask wearing, which is critical to the current COVID-19 pandemic. The presence or absence of a mask on a person greatly affects the efficacy of the social distancing protocols [32]. Similarly, interperson interactions such as hugs, kisses, and handshakes are more severe concerns than mere distancing amongst individuals [33, 34] as far as the person-to-person spreading of COVID-19 is concerned. The detection of mask-wearing [35, 36, 37, 38, 39] as well as the detection of dyadic interactions [40, 41, 42] has been explored in computer vision as isolated and distinct problems. However, to the best of the knowledge of the authors, those factors have not been incorporated into a unified and holistic solution for detecting violations of social distancing protocols in the literature Table 1. Ignoring such factors vastly undermines the robustness of vision-based techniques to tackle the social distancing problem of COVID-19. In this light, the system proposed in this paper analyzes the spatial and temporal interactions manifested over multiple frames. A single frame was analyzed to recognize how people adhere to social distancing measures such as keeping proper distance, mask wearing, and handshake interactions. The risk of spreading COVID-19 increases when an individual interacts with multiple people and the nature of the interaction. On the other hand, if a certain set of people are in a \"bubble\" and they remain so until the end of observation, there is no change in the risk of spreading COVID-19. This temporal analysis of identifying bio-bubbles is also included in our proposed model. In this paper, the design, implementation, and testing of a complete end-to-end system comprising of a framework to fit in different computer vision and deep learning-based techniques, a representation to store the output of the deep learning models, and an interpretation technique to evaluate the threat level of a given scene are discussed. The key contributions of this paper are as follows:  
* A deep learning-based system to monitor social distancing violations and COVID-19 threat parameters. The system can utilize multiple computer vision modules to extract different information from the video sequence such as the number of people, their location, their physical interactions, and whether they wear masks. * A temporal graph representation to structurally store the information extracted by the computer vision modules. In this representation, people are represented by nodes with time-varying properties for their location and behavior. The edges between people represent the interactions and social groups. * A methodology to interpret the graph and quantify the threat level in every scene based on primary and secondary threat parameters such as individual behavior, proximity, and group dynamics extracted from the graph representation.