## 1 Introduction  
COVID-19 is a viral infection that causes a wide range of complications, primarily in the respiratory system [1] along with other systems [2, 3]. As per current statistics, even though the virus has a comparatively small case mortality rate, it has amassed a massive fatality count due to its high infectiousness. The World Health Organization (WHO) estimates that the virus has infected around 338 million people and claimed more than 5.72 million lives as of December 2021. Despite the availability of effective vaccines against virus spread, medical complications, and mortality, complete global vaccination coverage is still far overdue. Furthermore, emerging variants cast some non-trivial obstacles to vaccine efficiency [4, 5, 6, 7]. Therefore, mitigating the spread of the disease through social distancing, mask wearing, hand washing, sanitizing, and other practices of hygiene still remains indispensable by and large [8, 9] to restore normalcy whilst ensuring the safety of health. People, being a social species, tend to exhibit group behaviors frequently. Therefore, even the most mindful persons may violate social distancing protocols occasionally [10, 11]. Even such occasional violation of social distancing protocols may garner a risk of contracting COVID-19 depending on the proximity or duration of the violation [12, 13]. Conversely, monitoring such violations of social distancing protocols (i.e., proximity, duration as well as the intensity of sudden events such as maskless cough or sneezing) provide vital tools for contact tracing, monitoring, and eventually pandemic control. In essence, observing social distancing protocol violations is a task with many caveats. Thus, automating this manual process needs meticulous analysis [14]. The main two avenues of research have been (a) intrusive solutions where people are actively contributing to the measurement (by handheld devices, etc.) and (b) non-intrusive solutions with zero burden on the people (which could be deployed to any situation irrespective of who is being monitored). The first type (intrusive techniques) requires a signal to be transmitted by the people being tracked; i.e., methods of this type require an active beacon by each tracked person. Such a wearable device based on an oscillating magnetic field for proximity sensing to monitor social distancing to prevent COVID-19 has been presented in [15]. This system was shown to be more robust than Bluetooth-sensing devices [16], especially in determining the distance threshold limit. However, it is practically difficult to deploy a solution of this type in a public space in a real-world situation. Thus, a non-intrusive solution is preferable for large-scale deployment in public spaces as the people who are being tracked are done so passively. Research in non-intrusive techniques to monitor social distancing has led to a large body of work utilizing computer vision techniques. The major sub-tasks in those approaches are the detection and tracking of people, and the state of the art for these sub-tasks is now primarily dominated by convolution neural networks (CNNs). Most recent applications combine YOLO [17] and deepSORT [18] to form powerful tools which can achieve object detection and tracking in real time, and it is used to tackle object recognition problems in different scenarios such as license plate recognition [19], road marking detection [20], pedestrian detection [21], agricultural production [22], etc. The work in [23] is an example of a CNN framework built on the aforementioned detection and localization algorithms to detect people, calculate the Euclidean distance between them and spot social distancing violations. A similar approach using YOLOv3 is performed in [24, 25] for birds-eye view (overhead) camera footage. However, such overhead viewpoints are not practically deployable in public settings. An SSD-based model is presented in [26], which also performs person detection and social distancing violation identification.