In pandemic surveillance, ensuring social distance has emerged as a challenging issue due to the lack of proper therapeutic agents, and this envisages the need for automated social distance monitoring to avoid the formation of social gatherings and free-standing conversation groups (FCGs). The robustness sought in detecting these groups cannot be achieved when there are illumination variation and occlusion among subjects by solely relying on video data from distributed cameras. In this paper, we propose a deep learning framework for integrating data from multiple sensor modalities taking into account the spatial properties necessary to manage illumination variation and occlusion of video data. From the fused data, social distance compliance violations are notified by the presence of social groups as graphs detected using a pre-trained deep framework and connected components in graph theory. A cost function is devised for social group graph clustering to identify FCGs by using the socio-psychological theory of Friends-formation. Experiment analysis on four benchmark datasets shows that the proposed approach excels at detecting social distance violations and FCGs, and succeeds in analyzing the potential risk of pandemic spread in an area by the calculation of violation scores and rate of violation.

在大流行监测中，由于缺乏合适的治疗剂，确保社交距离已成为一个具有挑战性的问题，这预示着需要进行自动社交距离监控，以避免形成社交聚会和独立对话组（FCG）。当仅依靠来自分布式摄像机的视频数据在对象之间存在照明变化和遮挡时，就无法实现检测这些组的鲁棒性。在本文中，我们提出了一种深度学习框架，用于整合来自多个传感器模态的数据，同时考虑到管理照明变化和视频数据遮挡所必需的空间特性。根据融合的数据，通过使用预先训练的深度框架和图论中的连接组件检测到的图，会通过社交组的存在来通知违反社会距离合规性的情况。针对社会群体图聚类，设计了一种成本函数，通过使用Friends-formation的社会心理理论来识别FCG。对四个基准数据集的实验分析表明，该方法在检测社会距离违规和FCG方面表现出色，并且通过计算违规得分和违规率成功地分析了某个地区大流行的潜在风险。