[论文解读] Social Distance Detection Using Deep Learning And Risk Management System

R. G. Sangeetha, Jaya Aravindh V. V|arXiv (Cornell University)|Apr 20, 2023

COVID-19 diagnosis using AI被引用 4

一句话总结

本文提出了一种基于深度学习的社交距离检测系统，采用单阶段目标检测与卷积神经网络（CNN）技术，通过闭路电视（CCTV）画面实现实时监控合规性。系统集成风险管理系统以分析违规行为，实现医院和学校等公共场所的自动化监控，减少对人工监控的依赖，同时在疫情期间提升安全性。

ABSTRACT

An outbreak of the coronavirus disease which occurred three years later and it has hit the world again with many evolutions. The effects on the human race have already been profound. We can only safeguard ourselves against this pandemic by mandating a "Face Mask" also maintaining the "Social Distancing." The necessity of protective face masks in all gatherings is required by many civil institutions in India. As a result of the substantial human resource utilization, personally examining the whole country with a huge population like India, to determine whether the execution of mask wearing and social distance maintained is unfeasible. The COVID-19 Social Distancing Detector System is a single-stage detector that employs deep learning to integrate high-end semantic data to a CNN module in order to maintain social distances and simultaneously monitor violations within a specified region. By deploying current Security footages, CCTV cameras, and computer vision (CV), it will also be able to identify those who are experiencing the calamity of social separation. Providing tools for safety and security, this technology disposes the need for a labor-force based surveillance system, yet a manual governing body is still required to monitor, track, and inform on the violations that are committed. Any sort of infrastructure, including universities, hospitals, offices of the government, schools, and building sites, can employ the technology. Therefore, the risk management system created to report and analyze video streams along with the social distance detector system might help to ensure our protection and security as well as the security of our loved ones. Furthermore, we will discuss about deployment and improvement of the project overall.

研究动机与目标

• 为解决印度等高人口区域人工监控社交距离的挑战。
• 减少在新冠疫情背景下执行公共卫生措施时对人力的依赖。
• 开发一种基于计算机视觉的自动化系统，以检测社交距离违规和口罩佩戴行为。
• 集成风险管理系统，用于报告和分析基于视频的违规行为。
• 实现在学校、医院和政府机构等各类机构中的可扩展部署。

提出的方法

• 系统采用基于卷积神经网络（CNN）架构的单阶段检测器，从视频帧中提取高层语义特征。
• 处理现有闭路电视（CCTV）摄像头的实时视频流，检测人员并测量人与人之间的距离。
• 系统应用在人员检测和空间关系理解方面训练的深度学习模型，以识别近距离违规行为。
• 风险管理系统记录并分析检测到的违规行为，支持追踪和报告，便于行政跟进。
• 该框架设计用于无缝集成到现有监控基础设施中，无需新增硬件。
• 支持在大学、医院和建筑工地等多种环境中的部署。

实验结果

研究问题

• RQ1 如何利用深度学习实现公共场所社交距离监控的自动化？
• RQ2 基于CNN的单阶段检测器在实时视频中检测近距离违规行为的有效性如何？
• RQ3 风险管理系统如何提升自动化检测在机构执法中的实用性？
• RQ4 该系统能否在无需重大基础设施变更的情况下有效部署于多样化的公共场所？
• RQ5 自动化能否显著减少人工监控的工作量？

主要发现

• 该系统利用现有闭路电视（CCTV）基础设施实现实时社交距离违规检测，显著减少人工监控需求。
• 风险管理系统的集成使得违规行为的记录与分析更加结构化。
• 该模型可部署于各类公共机构，包括学校、医院和政府办公室。
• 该方法支持同时检测口罩佩戴和社交距离行为，提升公共卫生合规性。
• 系统展现出良好的可扩展性和适应性，可在极少硬件升级的情况下适配不同环境。
• 该解决方案降低了运营成本，并提高了公共卫生措施执行的一致性。