Department of Computer Software Engineering, NUST, Islamabad, Pakistan.
PLoS One. 2021 Feb 25;16(2):e0247440. doi: 10.1371/journal.pone.0247440. eCollection 2021.
The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people's night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm.
这项工作的目的是在大流行期间的低光照环境中提供一种有效的社交距离监测解决方案。由 SARS-CoV-2 病毒引起的肆虐的 2019 年冠状病毒病（COVID-19）在全球范围内造成了致命的传播，带来了全球性危机。在没有有效治疗方法和疫苗的情况下，严格控制这种大流行主要依赖于个人预防措施，例如洗手、使用口罩、环境清洁，最重要的是保持社交距离，这是应对这种情况的唯一权宜之计。低光照环境可能会成为疾病传播的一个问题，因为人们在夜间聚集。特别是在全球气温达到峰值的夏季，情况可能会更加危急。在城市中，人们的住房拥挤，没有适当的空气交叉系统，因此他们会找到与家人一起在夜间外出呼吸新鲜空气的方法。在这种情况下，有必要采取有效措施来监测安全距离标准，以避免更多的阳性病例，并控制死亡人数。在本文中，提出了一种基于深度学习的解决方案来解决上述问题。所提出的框架利用了单次静止飞行时间（ToF）相机的 You Only Look Once v4（YOLO v4）模型进行实时目标检测，并引入了社交距离测量方法。根据计算出的距离指示风险因素，并突出显示违反安全距离的情况。实验结果表明，所提出的模型具有良好的性能，平均精度（mAP）得分为 97.84%，实际和测量社交距离值之间的观测平均绝对误差（MAE）为 1.01 厘米。
