Himeur Yassine, Al-Maadeed Somaya, Almaadeed Noor, Abualsaud Khalid, Mohamed Amr, Khattab Tamer, Elharrouss Omar
Computer Science and Engineering Department, Qatar University, Qatar.
Electrical Engineering Department, Qatar University, Qatar.
Sustain Cities Soc. 2022 Oct;85:104064. doi: 10.1016/j.scs.2022.104064. Epub 2022 Jul 21.
Since the start of the COVID-19 pandemic, social distancing (SD) has played an essential role in controlling and slowing down the spread of the virus in smart cities. To ensure the respect of SD in public areas, visual SD monitoring (VSDM) provides promising opportunities by (i) controlling and analyzing the physical distance between pedestrians in real-time, (ii) detecting SD violations among the crowds, and (iii) tracking and reporting individuals violating SD norms. To the authors' best knowledge, this paper proposes the first comprehensive survey of VSDM frameworks and identifies their challenges and future perspectives. Typically, we review existing contributions by presenting the background of VSDM, describing evaluation metrics, and discussing SD datasets. Then, VSDM techniques are carefully reviewed after dividing them into two main categories: hand-crafted feature-based and deep-learning-based methods. A significant focus is paid to convolutional neural networks (CNN)-based methodologies as most of the frameworks have used either one-stage, two-stage, or multi-stage CNN models. A comparative study is also conducted to identify their pros and cons. Thereafter, a critical analysis is performed to highlight the issues and impediments that hold back the expansion of VSDM systems. Finally, future directions attracting significant research and development are derived.
自新冠疫情爆发以来，社交距离（SD）在智慧城市中控制和减缓病毒传播方面发挥了至关重要的作用。为确保在公共场所遵守社交距离，视觉社交距离监测（VSDM）通过以下方式提供了有前景的机会：（i）实时控制和分析行人之间的物理距离；（ii）检测人群中的社交距离违规行为；（iii）跟踪和报告违反社交距离规范的个人。据作者所知，本文首次对VSDM框架进行了全面综述，并确定了其面临的挑战和未来前景。具体而言，我们通过介绍VSDM的背景、描述评估指标以及讨论社交距离数据集来回顾现有研究成果。然后，在将VSDM技术分为两大类后对其进行了仔细回顾：基于手工特征的方法和基于深度学习的方法。由于大多数框架都使用了单阶段、两阶段或多阶段卷积神经网络（CNN）模型，因此重点关注了基于CNN的方法。还进行了一项比较研究以确定它们的优缺点。此后，进行了批判性分析以突出阻碍VSDM系统扩展的问题和障碍。最后，得出了吸引大量研发的未来方向。
