Estimation of crowd density applying wavelet transform and machine learning

被引：25

作者：

Nagao, Koki ^{[1
]}

Yanagisawa, Daichi ^{[2
]}

Nishinari, Katsuhiro ^{[2
]}

机构：

[1] Univ Tokyo, Sch Engn, Dept Aeronaut & Astronaut, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138656, Japan

[2] Univ Tokyo, Res Ctr Adv Sci & Technol, Meguro Ku, 4-6-1 Komaba, Tokyo 1538904, Japan

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2018年 / 510卷

关键词：

Density estimation; Tablet sensor; Wavelet transform; Machine learning; Real experiment; PEDESTRIAN FACILITIES; SERVICE; LEVEL; FLOW;

D O I：

10.1016/j.physa.2018.06.078

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We conducted a simple experiment in which one pedestrian passed through a crowded area and measured the body-rotational angular velocity with commercial tablets. Then, we developed a new method for predicting crowd density by applying the continuous wavelet transform and machine learning to the data obtained in the experiment. We found that the accuracy of prediction using angular velocity data was as high as that using raw velocity data. Therefore, we concluded that angular velocity has relationship with crowd density and we could estimate crowd density by angular velocity. Our research will contribute to management of safety and comfort of pedestrians by developing an easy way to measure crowd density. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：145 / 163

页数：19

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