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

机译:基于深度学习的蜂窝通信中的人群密度估计系统

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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.
机译:近年来,由于人流的方向信息对于各种目的(例如,导航,疏散)非常有用,因此人流观察系统的研究和开发在各个领域(例如市区,购物区)引起了关注。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,但是由于隐私问题,在实际领域中使用摄像机并不是可取的。因此,在这项研究中,我们提出了一个新的研究对象人群密度观察系统,用于人流观察。为了避免隐私问题,所提出的系统仅测量蜂窝通信的无线电波的信号强度。此外,通过利用几种机器学习技术来分析测量结果,以便估计许多拥有手机或智能手机的人的人群密度。

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