Wi-Count:使用COTS WiFi设备进行人流计数

Yanni Yang, Jiannong Cao, Xuefeng Liu, Xiulong Liu
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引用次数: 31

摘要

人口统计提供了关于人口流动和人类动态的有价值的信息,对智能人群控制和零售管理起着至关重要的作用。由于人的存在会影响无线信号的传播,因此可以从无线信号中提取移动人群的信息,近年来通过射频信号实现了人群计数。然而,现有的大多数使用无线信号的研究只适用于人们一直在移动的情况。此外,他们需要劳动密集的培训阶段,以建立计数模型。在Wi-Count系统中,我们采用了另一种方法,即对经过门口的使用COTS WiFi设备的人数进行计数。它不仅可以检测通过方向,还可以在多人同时通过的情况下识别人数,而无需调节通过行为和预训练计数模型。通过模拟双向传递行为对WiFi信号相位差的影响来识别传递方向。此外,通过一种增强的信号分离算法来获得通过的人数,以提供精确的计数结果。大量实验表明,该算法在传球方向检测和传球人数统计上的平均准确率分别达到95%和92%左右。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wi-Count: Passing People Counting with COTS WiFi Devices
People counting provides valuable information on population mobility and human dynamics, which plays a critical role for intelligent crowd control and retail management. Recently, people counting has been achieved via radio-frequency signals as human presence can influence the propagation of wireless signals, from which the information of the moving crowd can be extracted. However, most of the existing studies using wireless signals only apply to the scenario when people keep moving all the time. Besides, they require labour-intensive training phase for building the counting model. In the Wi-Count system, we take another approach, which is to count the people passing by the doorway with COTS WiFi devices. It can not only detect the passing direction, but also identify the number of people even when multiple persons pass by concurrently without regulating passing behavior and pre-trained counting model. The passing direction is recognized by modeling the effects of the bi-directional passing behavior on the phase difference of WiFi signals. In addition, the number of passing people is obtained through an enhanced signal separation algorithm for providing precise counting result. Extensive experiments show the average accuracy on passing direction detection and passing people counting are about 95% and 92% respectively.
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