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用于监测公共场所出入情况的可扩展重力监测系统的工作原理和性能。

Working Principle and Performance of a Scalable Gravimetric System for the Monitoring of Access to Public Places.

机构信息

Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy.

Rete Ferroviaria Italiana S.p.A. Direzione Protezione Aziendale, Piazza della Croce Rossa 1, 00161 Roma, Italy.

出版信息

Sensors (Basel). 2020 Dec 17;20(24):7225. doi: 10.3390/s20247225.

Abstract

Here, we propose a novel application of a low-cost robust gravimetric system for public place access monitoring purposes. The proposed solution is intended to be exploited in a multi-sensor scenario, where heterogeneous information, coming from different sources (e.g., metal detectors and surveillance cameras), are collected in a central data fusion unit to obtain a more detailed and accurate evaluation of notable events. Specifically, the word "notable" refers essentially to two event categories: the first category is represented by irregular events, corresponding typically to multiple people passing together through a security gate; the second category includes some event subsets, whose notification can be interesting for assistance provision (in the case of people with disabilities), or for statistical analysis. The employed gravimetric sensor, compared to other devices existing in the literature, exhibits a simple scalable robust structure, made up of an array of rigid steel plates, each laid on four load cells. We developed a tailored hardware and software to individually acquire the load cell signals, and to post-process the data to formulate a classification of the notable events. The results are encouraging, showing a remarkable detectability of irregularities (95.3% of all the test cases) and a satisfactory identification of the other event types.

摘要

在这里,我们提出了一种低成本、鲁棒的称重系统在公共场所访问监控方面的新应用。该解决方案旨在多传感器场景中得到利用,来自不同来源(例如金属探测器和监控摄像机)的异构信息在中央数据融合单元中进行收集,以对显著事件进行更详细和准确的评估。具体来说,“显著”一词主要指两类事件:第一类是不规则事件,通常对应于多人一起通过安检门;第二类包括一些事件子集,通知这些事件子集可能有助于提供援助(针对残疾人士),或者进行统计分析。与文献中存在的其他设备相比,所采用的称重传感器具有简单、可扩展的鲁棒结构,由刚性钢板阵列组成,每个钢板放置在四个称重传感器上。我们开发了定制的硬件和软件,以单独采集称重传感器信号,并对数据进行后处理,以对显著事件进行分类。结果令人鼓舞,显示出不规则事件的显著检测能力(所有测试案例的 95.3%)和其他事件类型的令人满意的识别能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0761/7767313/12ed0eb421f1/sensors-20-07225-g001.jpg

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