Towards Fast and Accurate Intimate Contact Recognition through Video Analysis

Yuhao Luo, Hengjing Zhang, Hengchang Liu
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Abstract

Intimate contact recognition has gained more attention in academia field in recent years due to the outbreak of Covid-19. However, state of the art solutions suffer from either inefficient accuracy or high cost. In this paper, we propose a novel method for COVID-19 intimate contact recognition in public spaces through video camera networks (CCTV). This method leverages distance detection and re-Identification algorithms, so pedestrians in close contact are re-identified, their identity information is obtained and stored in a database to realize contact tracing. We compare different social distance detection algorithms and the Faster-RCNN model outperforms other al-ternatives in terms of running speed. We also evaluate our Re-Identification model on two types of indicators in the PETS2009 dataset: mAP reaches 85.1%; rank-1, rank-5, and rank-10 reach 97.8%, 98.9%, and 98.9%, respectively. Experimental results demonstrate that our solution can be effectively applied in public places to realize fast and accurate automatic contact tracing.
通过视频分析实现快速准确的亲密接触识别
近年来,新型冠状病毒感染症(Covid-19)的爆发,引起了学术界的广泛关注。然而,最先进的解决方案要么精度低,要么成本高。本文提出了一种利用闭路电视网络(CCTV)识别公共空间COVID-19亲密接触者的新方法。该方法利用距离检测和再识别算法,对近距离接触的行人进行再识别,获取其身份信息并存储在数据库中,实现接触追踪。我们比较了不同的社交距离检测算法,fast - rcnn模型在运行速度方面优于其他替代算法。在PETS2009数据集的两类指标上对我们的再识别模型进行了评价:mAP达到85.1%;Rank-1, rank-5, rank-10分别达到97.8%,98.9%,98.9%。实验结果表明,该方法可以有效地应用于公共场所,实现快速、准确的自动接触追踪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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