The ongoing COVID-19 corona virus outbreak has caused a global disaster with its deadly spreading. Due to the absence of effective remedial agents and the shortage of immunizations against the virus, population vulnerability increases. In the current situation, as there are no vaccines available; therefore, social distancing is thought to be an adequate precaution (norm) against the spread of the pandemic virus. The risks of virus spread can be minimized by avoiding physical contact among people. The purpose of this work is, therefore, to provide a deep learning platform for social distance tracking using an overhead perspective. The framework uses the YOLOv3 object recognition paradigm to identify humans in video sequences. The transfer learning methodology is also implemented to increase the accuracy of the model. In this way, the detection algorithm uses a pre-trained algorithm that is connected to an extra trained layer using an overhead human data set. The detection model identifies peoples using detected bounding box information. Using the Euclidean distance, the detected bounding box centroid's pairwise distances of people are determined. To estimate social distance violations between people, we used an approximation of physical distance to pixel and set a threshold. A violation threshold is established to evaluate whether or not the distance value breaches the minimum social distance threshold. In addition, a tracking algorithm is used to detect individuals in video sequences such that the person who violates/crosses the social distance threshold is also being tracked. Experiments are carried out on different video sequences to test the efficiency of the model. Findings indicate that the developed framework successfully distinguishes individuals who walk too near and breaches/violates social distances; also, the transfer learning approach boosts the overall efficiency of the model. The accuracy of 92% and 98% achieved by the detection model without and with transfer learning, respectively. The tracking accuracy of the model is 95%.

持续爆发的新型冠状病毒肺炎 (COVID-19) 病毒的致命传播已造成全球灾难。由于缺乏有效的治疗药物和缺乏针对该病毒的免疫接种，人口的脆弱性增加。在目前的情况下，由于没有疫苗可用；因此，社交距离被认为是防止大流行病毒传播的充分预防措施（规范）。通过避免人与人之间的身体接触可以最大限度地降低病毒传播的风险。因此，这项工作的目的是提供一个使用俯视视角进行社交距离跟踪的深度学习平台。该框架使用 YOLOv3 对象识别范例来识别视频序列中的人类。还实施了迁移学习方法来提高模型的准确性。通过这种方式，检测算法使用预先训练的算法，该算法使用开销人类数据集连接到额外训练的层。检测模型使用检测到的边界框信息来识别人员。使用欧几里得距离，确定检测到的边界框质心的人的成对距离。为了估计人与人之间的社交距离违规情况，我们使用了像素物理距离的近似值并设置了阈值。建立违规阈值来评估距离值是否违反最小社交距离阈值。此外，跟踪算法用于检测视频序列中的个人，以便违反/超过社交距离阈值的人也被跟踪。在不同的视频序列上进行实验来测试模型的效率。研究结果表明，所开发的框架成功区分了走得太近和违反/违反社交距离的个人；此外，迁移学习方法还提高了模型的整体效率。不使用迁移学习和使用迁移学习的检测模型的准确率分别达到 92% 和 98%。模型的跟踪准确率为95%。