People Crowd Density Estimation System using Deep Learning for Radio Wave Sensing of Cellular Communication

Kyosuke Shibata, Hiroshi Yamamoto
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引用次数: 9

Abstract

In recent years, research and development of a people flow observation system is attracting attention in various fields (e.g., city area, shopping district) because the directional information of people flow is very useful for various objective (e.g., navigation, evacuation). However, existing studies of the observation system have mainly been utilizing cameras and image analysis techniques for specifying people flow, but the use of cameras is not preferable in actual fields because of the privacy issues.Therefore, in this study, we propose a new people crowd density observation system for people flow observation. In order to avoid privacy issues, the proposed system dmeasures only signal strength of radio waves of the cellular communication. Furthermore, the measurement results are analyzed by utilizing several machine learning techniques so as to estimate crowd density of many people who have a mobile phone or a smartphone.
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基于深度学习的蜂窝通信无线电波感知人群密度估计系统
近年来,人流观测系统的研究和开发受到了各个领域(如城市区域、购物区)的关注,因为人流的方向信息对各种目标(如导航、疏散)都有很大的帮助。然而,现有的观测系统研究主要是利用摄像机和图像分析技术来指定人流,但由于隐私问题,在实际领域中使用摄像机并不可取。因此,在本研究中,我们提出了一种新的人群密度观测系统,用于人流观测。为了避免隐私问题,该系统仅测量蜂窝通信无线电波的信号强度。此外,通过使用几种机器学习技术对测量结果进行分析,以估计拥有手机或智能手机的许多人的人群密度。
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