Social distance in object detection: Survey based on cutting-edge deep learning approach

Many health systems face extraordinary problems because of the ongoing COVID-19 outbreak and new variations. Multiple regulatory authorities have made it mandatory to maintain a safe distance, particularly in public settings where large groups of people are likely to come into contact, such as sports arenas, public transportation, workplaces as well as shopping malls. Nevertheless, keeping a safe distance (two meters), adjusting multiple model detection errors or accuracy as well as deployment prerequisites, great number of people, facial expression, view angle, low-resolution images, detection model deployment on computers having restricted processing power, and the shortage of a real-world dataset have all made compliance and adherence to proper distancing social difficult. As a result, this survey examines and contrasts the most important past deep learning (DL)-based social distance research. Here, the survey presents a new fine-grained taxonomy that classifies the present state-of-the-art DL-based object detection for detecting distance in terms of several dimensions, such as detection, input data, evaluation methodologies, as well as testing, based on a thorough review. Each facet is then divided into categories based on a variety of factors. In addition, this survey analyses and evaluates the associated experimental techniques suggested as DL-based object detection. Finally, this survey examines DL's role in social distance, object detection datasets impact, as well as the proposed approaches efficacy by assessing the experimental research. The results show that more work is needed to enhance the existing state-of-the-art. Ultimately, open research difficulties are recognized, as well as prospective DL research areas are suggested for future research.