The escalating transmission intensity of COVID-19 pandemic is straining the healthcare systems worldwide. Due to the unavailability of effective pharmaceutical treatment and vaccines, monitoring social distancing is the only viable tool to strive against asymptomatic transmission. Pertaining to the need of monitoring the social distancing at populated areas, a novel bird eye view computer vision-based framework implementing deep learning and utilizing surveillance video is proposed. This proposed method employs YOLO v3 object detection model and uses key point regressor to detect the key feature points. Additionally, as the massive crowd is detected, the bounding boxes on objects are received, and red boxes are also visible if social distancing is violated. When empirically tested over real-time data, proposed method is established to be efficacious than the existing approaches in terms of inference time and frame rate.

COVID-19 大流行的传播强度不断升级，正在给全球医疗保健系统带来压力。由于缺乏有效的药物治疗和疫苗，监测社会距离是对抗无症状传播的唯一可行工具。针对监控人口稠密地区社会距离的需要，提出了一种基于鸟瞰计算机视觉的新型框架，该框架实现了深度学习和利用监控视频。该方法采用 YOLO v3 对象检测模型，并使用关键点回归器来检测关键特征点。此外，当检测到大量人群时，会接收到物体上的边界框，如果违反社交距离，红色框也会可见。当对实时数据进行经验测试时，