The COVID-19 has been posing threats to peoples health around the world due to its simple transmission route, strong mutation ability, and high infection. Currently, the superb vaccine breakthrough ability of mutant strains puts people's lives and health further at risk. Many measures are taken to fight against the epidemic. Keeping a safe social distance to prevent the spread of COVID-19 in public places. In this paper, we propose a method to monitor social distancing from a bird's-eye view. It mainly includes three parts. Firstly, an objector based on YOLOv5m is trained to detect human beings. It shows good performance compared with other models such as Faster R-CNN and YOLOv3. Secondly, we propose to use SORT algorithm to track each human by assigning it an ID. It also ensures the accuracy of multiple-object tracking. More importantly, it makes for tracking those who violate social distance. Thirdly, the Euclidean distance between detected people is used to judge whether human beings keep social distance or not. It is implemented by computing the pairwise distances of the detected bounding box centroids. Experiments conducted on birds view dataset show that our method can monitor social distance well. Hopefully the proposed method and dataset can provide some help in the fighting against COVID-19.