COVID-19 is a virus, which is transmitted through small droplets during speech, sneezing, coughing, and mostly by inhalation between individuals in close contact. The pandemic is still ongoing and causes people to have an acute respiratory infection which has resulted in many deaths. The risks of COVID-19 spread can be eliminated by avoiding physical contact among people. This research proposes real-time AI platform for people detection, and social distancing classification of individuals based on thermal camera. YOLOv4-tiny is proposed in this research for object detection. It is a simple neural network architecture, which makes it suitable for low-cost embedded devices. The proposed model is a better option compared to other approaches for real-time detection. An algorithm is also implemented to monitor social distancing using a bird’s-eye perspective. The proposed approach is applied to videos acquired through thermal cameras for people detection, social distancing classification, and at the same time measuring the skin temperature for the individuals. To tune up the proposed model for individual detection, the training stage is carried out by thermal images with various indoor and outdoor environments. The final prototype algorithm has been deployed in a low-cost Nvidia Jetson devices (Xavier and Jetson Nano) which are composed of fixed camera. The proposed approach is suitable for a surveillance system within sustainable smart cities for people detection, social distancing classification, and body temperature measurement. This will help the authorities to visualize the fulfillment of the individuals with social distancing and simultaneously monitoring their skin temperature.

COVID-19 是一种病毒，在说话、打喷嚏、咳嗽时通过小飞沫传播，并且主要通过密切接触者之间的吸入传播。大流行仍在持续，并导致人们患有急性呼吸道感染，导致许多人死亡。避免人与人之间的身体接触可以消除 COVID-19 传播的风险。这项研究提出了实时人工智能平台，用于人员检测和基于热像仪的个人社交距离分类。本研究提出了 YOLOv4-tiny 用于目标检测。它是一种简单的神经网络架构，使其适用于低成本的嵌入式设备。与其他实时检测方法相比，所提出的模型是一个更好的选择。还实施了一种算法，以使用鸟瞰视角监控社交距离。所提出的方法适用于通过热像仪获取的视频，用于人员检测、社会距离分类，同时测量个人的皮肤温度。为了调整所提出的个体检测模型，训练阶段通过各种室内和室外环境的热图像进行。最终的原型算法已部署在由固定摄像头组成的低成本 Nvidia Jetson 设备（Xavier 和 Jetson Nano）中。所提出的方法适用于可持续智慧城市内的监控系统，用于人员检测、社会距离分类和体温测量。