Saponara Sergio, Elhanashi Abdussalam, Zheng Qinghe
Dip. Ingegneria Informazione, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy.
School of Information Science and Engineering, Shandong University, Jinan, China.
J Real Time Image Process. 2022;19(3):551-563. doi: 10.1007/s11554-022-01203-5. Epub 2022 Feb 22.
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.
新冠病毒是一种通过说话、打喷嚏、咳嗽时产生的小飞沫传播的病毒，在密切接触的个体之间主要通过吸入传播。这场大流行仍在持续，导致人们患上急性呼吸道感染，造成了许多死亡。避免人与人之间的身体接触可以消除新冠病毒传播的风险。本研究提出了一种基于热成像摄像头的用于人员检测和个体社交距离分类的实时人工智能平台。本研究中提出使用YOLOv4-tiny进行目标检测。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。还实现了一种算法，从鸟瞰视角监测社交距离。所提出的方法应用于通过热成像摄像头获取的视频，用于人员检测、社交距离分类，同时测量个体的皮肤温度。为了调整所提出的个体检测模型，训练阶段使用了各种室内和室外环境的热成像图像。最终的原型算法已部署在由固定摄像头组成的低成本英伟达Jetson设备（Xavier和Jetson Nano）中。所提出的方法适用于可持续智慧城市中的监控系统，用于人员检测、社交距离分类和体温测量。这将有助于当局直观了解个体是否遵守社交距离规定，同时监测他们的皮肤温度。
