DepTSol: An Improved Deep-Learning- and Time-of-Flight-Based Real-Time Social Distance Monitoring Approach under Various Low-Light Conditions

Abstract

Social distancing is an utmost reliable practice to minimise the spread of coronavirus disease (COVID-19). As the new variant of COVID-19 is emerging, healthcare organisations are concerned with controlling the death and infection rates. Different COVID-19 vaccines have been developed and administered worldwide. However, presently developed vaccine quantity is not sufficient to fulfil the needs of the world’s population. The precautionary measures still rely on personal preventive strategies. The sharp rise in infections has forced governments to reimpose restrictions. Governments are forcing people to maintain at least 6 feet (ft) of safe physical distance to stay safe. With summers, low-light conditions can become challenging. Especially in the cities of underdeveloped countries, where poor ventilated and congested homes cause people to gather in open spaces such as parks, streets, and markets. Besides this, in summer, large friends and family gatherings mostly take place at night. It is necessary to take precautionary measures to avoid more drastic results in such situations. To support the law and order bodies in maintaining social distancing using Social Internet of Things (SIoT), the world is considering automated systems. To address the identification of violations of a social distancing Standard Operating procedure (SOP) in low-light environments via smart, automated cyber-physical solutions, we propose an effective social distance monitoring approach named DepTSol. We propose a low-cost and easy-to-maintain motionless monocular time-of-flight (ToF) camera and deep-learning-based object detection algorithms for real-time social distance monitoring. The proposed approach detects people in low-light environments and calculates their distance in terms of pixels. We convert the predicted pixel distance into real-world units and compare it with the specified safety threshold value. The system highlights people violating the safe distance. The proposed technique is evaluated by COCO evaluation metrics and has achieved a good speed–accuracy trade-off with 51.2 frames per second (fps) and a 99.7% mean average precision (mAP) score. Besides the provision of an effective social distance monitoring approach, we perform a comparative analysis between one-stage object detectors and evaluate their performance in low-light environments. This evaluation will pave the way for researchers to study the field further and will enlighten the efficiency of deep-learning algorithms in timely responsive real-world applications.

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

“…Because low-light environments can result in spreading COVID-19, developing efficient VSDM schemes that address this issue is of utmost importance. To that end, Rahim, Maqbool, Mirza, Afzal, and Asghar (2022) introduce DepTSol, which is a CSP-ized YOLOv4-based VSDM system under different light conditions. It also enables the monitoring of pedestrians at varying camera distances.…”

“…To highlight the performance of one-stage pedestrian detectors for VSDM under different light conditions, seven detectors are evaluated on the ExDARK dataset to assess the accuracy and speed of their models, as explained in Rahim et al (2022) . Fig.…”

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Fig. 6 The computational cost evaluation of different pedestrian detectors in terms of the fps score ( Rahim et al, 2022 ).

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Recent Advances in Internet of Things and Emerging Social Internet of Things: Vision, Challenges and Trends

“…Rahim et al [2] proposed a deep learning-based model for the monitoring of COVID social distance violations. Currently, the standard operating procedure (SoP) for social distance is one of the good methods to mimic the coronavirus (COVID-19).…”