A Depth Camera-based Warning System Design for Social distancing Detection
Zijun Wang, Bo Wu, Kiminori Sato · 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · 2021
Nowadays, COVID-19 is raging around the world. Because of its highly contagious, people have to take many measures and change their daily lifestyle to face it. Keeping social distancing is particularly important for the prevention of COVID-19, especially for the administrator of public spaces, it makes sense to urge people to maintain social distancing. However, if the administrator directly carries out social distancing management, it will consume a lot of manpower and material resources, it is necessary to design a system that can automatically detect social distancing status in the areas. For the studies of social distancing detection, most of the related works use pixel analysis techniques based on images to obtain distance data, but this type of technique may produce large errors due to the difference camera angles. Therefore, in this paper, we plan to present a design of the social distancing detection and warning system by using the devices of high-precision depth camera and Android-based smart glasses. By using the depth camera, we can obtain the distance data more accurately to prevent misjudgment due to insufficient information acquisition, in addition, the using of smart glasses as the information terminal in order to provide relevant social distancing warning information to the area administrators more quickly and accurately. This system will not only benefit area administrators directly, but will also provide the basis for research in the area of social distancing risk in public places.