Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America.
Department of Computer Science, University of Maryland, College Park, Maryland, United States of America.
PLoS One. 2021 Dec 1;16(12):e0259713. doi: 10.1371/journal.pone.0259713. eCollection 2021.
Observing social/physical distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not maintaining social distancing, i.e. about 2 meters of space between them using an autonomous mobile robot and existing CCTV (Closed-Circuit TeleVision) cameras. The robot is equipped with commodity sensors, namely an RGB-D (Red Green Blue-Depth) camera and a 2-D lidar to detect social distancing breaches within their sensing range and navigate towards the location of the breach. Moreover, it discreetly alerts the relevant people to move apart by using a mounted display. In addition, we also equip the robot with a thermal camera that transmits thermal images to security/healthcare personnel who monitors COVID symptoms such as a fever. In indoor scenarios, we integrate the mobile robot setup with a static wall-mounted CCTV camera to further improve the number of social distancing breaches detected, accurately pursuing walking groups of people etc. We highlight the performance benefits of our robot + CCTV approach in different static and dynamic indoor scenarios.
观察人类之间的社交/物理距离规范已经成为减缓 COVID-19 传播的不可或缺的预防措施。我们提出了一种新的方法，使用自主移动机器人和现有的闭路电视（CCTV）摄像机自动检测拥挤场景中未保持社交距离的人类对，即彼此之间大约 2 米的空间。机器人配备了商品传感器，即 RGB-D（红、绿、蓝-深度）摄像机和 2-D 激光雷达，以检测其感应范围内的社交距离违规行为，并导航到违规地点。此外，它还通过安装的显示器 discreetly 提醒相关人员分开。此外，我们还为机器人配备了热像仪，将热图像传输给监控 COVID 症状（如发烧）的安全/医疗保健人员。在室内场景中，我们将移动机器人设置与静态壁挂式 CCTV 摄像机集成在一起，以进一步提高检测到的社交距离违规数量，准确地追踪行走的人群等。我们强调了我们的机器人+CCTV 方法在不同静态和动态室内场景中的性能优势。
