A method of pedestrian flow monitoring based on received signal strength
A method of pedestrian flow monitoring based on received signal strength
复制标题
一种基于接收信号强度的人流监测方法
DOI：
10.1186/s13638-021-02079-y
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发表时间：
2021-02
影响因子：
2.6
通讯作者：
Kaide Huang
中科院分区：
文献类型：
--
作者：
Zhiyong Yang;Jing Wen;Kaide Huang
摘要在风景名胜区、商场、车站、广场等大型公共场所，对人员统计和人流监测有着广泛的需求。根据人流监测系统的反馈，可以优化配置资源，实现社会效益和经济效益的最大化。此外，由于及时进行行人引导，可以避免踩踏事故。为了满足这些要求，我们提出了一种基于无线传感器网络接收信号强度(RSS)的行人流量监测方法。该方法主要利用行人对有效路段射频信号的阴影衰减效应。本文首先设计了一种用于行人监控的射频无线传感器网络部署结构。其次，对RSS信号序列进行短时间的小波分解，提取特征。最后，利用实验数据集对支持向量机算法进行训练，以识别通过监测点的瞬时行人数量。在室内人员密度密集和稀疏的情况下，支持向量机模型的准确率分别为88.9%和94.5%。在室外环境下，支持向量机模型的准确率为92.9%。实验结果表明，在行人流量实时监控的背景下，该方法可以实现高精度的人流监控。
AbstractThere is a wide demand for people counting and pedestrian flow monitoring in large public places such as scenic tourist areas, shopping malls, stations, squares, and so on. Based on the feedback from the pedestrian flow monitoring system, resources can be optimally allocated to maximize social and economic benefits. Moreover, trampling accidents can be avoided because pedestrian guidance is carried out in time. In order to meet these requirements, we propose a method of pedestrian flow monitoring based on the received signal strength (RSS) of wireless sensor networks. This method mainly utilizes the shadow attenuation effect of pedestrians on radio frequency (RF) signals of effective links. In this paper, a deployment structure of RF wireless sensor network is firstly designed to monitor the pedestrians. Secondly, the features are extracted from the wavelet decomposition of RSS signal series with a short time. Lastly, the support vector machine (SVM) algorithm is trained by an experimental data set to distinguish the instantaneous number of pedestrian passing through the monitoring point. In the case of dense and sparse indoor personnel density, the accuracy of the SVM model is 88.9% and 94.5%, respectively. In the outdoor environment, the accuracy of the SVM model is 92.9%. The experimental results show that this method can realize the high precision monitoring of the flow of people in the context of real-time pedestrian flow monitoring.
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