In the current times, the fear and danger of COVID-19 virus still stands
large. Manual monitoring of social distancing norms is impractical with a large
population moving about and with insufficient task force and resources to
administer them. There is a need for a lightweight, robust and 24X7
video-monitoring system that automates this process. This paper proposes a
comprehensive and effective solution to perform person detection, social
distancing violation detection, face detection and face mask classification
using object detection, clustering and Convolution Neural Network (CNN) based
binary classifier. For this, YOLOv3, Density-based spatial clustering of
applications with noise (DBSCAN), Dual Shot Face Detector (DSFD) and
MobileNetV2 based binary classifier have been employed on surveillance video
datasets. This paper also provides a comparative study of different face
detection and face mask classification models. Finally, a video dataset
labelling method is proposed along with the labelled video dataset to
compensate for the lack of dataset in the community and is used for evaluation
of the system. The system performance is evaluated in terms of accuracy, F1
score as well as the prediction time, which has to be low for practical
applicability. The system performs with an accuracy of 91.2% and F1 score of
90.79% on the labelled video dataset and has an average prediction time of 7.12
seconds for 78 frames of a video.

本文提出了一种基于目标检测、聚类和卷积神经网络的轻量级、稳健且全天候自动化视频监控系统，用于检测人体、社交距离违规、人脸及口罩分类，并提供了不同人脸检测和口罩分类模型的比较研究和基于视频标记的数据集标记方法，该系统在标记视频数据集上的准确率为 91.2％，F1 得分为 90.79％，预测时间平均为 7.12 秒。