Customer classification method for multiple pedestrians using pressure sensors

Junjirou Hasegawa, Takuya Tajima, Takehiko Abe, Haruhiko Kimura
{"title":"Customer classification method for multiple pedestrians using pressure sensors","authors":"Junjirou Hasegawa, Takuya Tajima, Takehiko Abe, Haruhiko Kimura","doi":"10.1109/ICAWST.2013.6765464","DOIUrl":null,"url":null,"abstract":"This paper aims to customer classification age groups by walking data using pressure sensor array. Techniques of the age groups estimation in many retail businesses (for example, convenience stores, supermarkets, shopping malls, etc.) are marketable. Because those techniques are applicable for market research and customer analysis. There are many researches of the age estimation using face images, walking silhouette data, etc. However, there are some problems too. One of problem is that the estimation classes are a few. Moreover, many age estimation systems use some video cameras. Therefore, these systems may invade surveyed person's privacy by taking one's face images. Obviously the pressure sensor does not record one's face images. In this study, this fact is one of merit in using the pressure sensors. The pressure sensor array gets feature quantity including center of gravity, pressure value, area of foot, etc. In this study, our system classifies surveyed persons to 6 age groups as each decade. Here, surveyed persons are 20s-70s. Average estimation accuracy of all age groups is 82.67%. The highest estimation accuracy is 89.33% at 20s and 60s.","PeriodicalId":68697,"journal":{"name":"炎黄地理","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"炎黄地理","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1109/ICAWST.2013.6765464","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This paper aims to customer classification age groups by walking data using pressure sensor array. Techniques of the age groups estimation in many retail businesses (for example, convenience stores, supermarkets, shopping malls, etc.) are marketable. Because those techniques are applicable for market research and customer analysis. There are many researches of the age estimation using face images, walking silhouette data, etc. However, there are some problems too. One of problem is that the estimation classes are a few. Moreover, many age estimation systems use some video cameras. Therefore, these systems may invade surveyed person's privacy by taking one's face images. Obviously the pressure sensor does not record one's face images. In this study, this fact is one of merit in using the pressure sensors. The pressure sensor array gets feature quantity including center of gravity, pressure value, area of foot, etc. In this study, our system classifies surveyed persons to 6 age groups as each decade. Here, surveyed persons are 20s-70s. Average estimation accuracy of all age groups is 82.67%. The highest estimation accuracy is 89.33% at 20s and 60s.
使用压力传感器的多行人客户分类方法
本文的目的是利用压力传感器阵列的步行数据对客户进行年龄组分类。许多零售企业(如便利店、超市、购物中心等)的年龄组估计技术是有市场的。因为这些技术适用于市场调查和客户分析。利用人脸图像、行走轮廓等数据进行年龄估计的研究较多。然而,也有一些问题。问题之一是估计类很少。此外,许多年龄估计系统使用一些摄像机。因此,这些系统可能会通过拍摄被调查者的面部图像来侵犯被调查者的隐私。显然,压力传感器不会记录一个人的面部图像。在本研究中,这一事实是使用压力传感器的优点之一。压力传感器阵列得到的特征量包括重心、压力值、足部面积等。在这项研究中,我们的系统将调查对象分为6个年龄组,每10年。这里的调查对象是20 -70岁。各年龄组的平均估计准确率为82.67%。在20s和60s的估计精度最高,为89.33%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
784
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信