Counting Human Objects Using Backscattered Radio Frequency Signals

被引:21
作者
Ding, Han [1 ,2 ]
Han, Jinsong [3 ]
Liu, Alex X. [4 ]
Xi, Wei [1 ]
Zhao, Jizhong [1 ]
Yang, Panlong [5 ]
Jiang, Zhiping [6 ]
机构
[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Shaanxi, Peoples R China
[2] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China
[3] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Shaanxi Prov Key Lab Comp Network, Xian, Shaanxi, Peoples R China
[4] Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA
[5] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei, Anhui, Peoples R China
[6] Xidian Univ, Sch Software Engn, Xian, Shaanxi, Peoples R China
基金
中国博士后科学基金;
关键词
RFID; crowd counting;
D O I
10.1109/TMC.2018.2852627
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variation in the RSS values of the tag backscattered RF signals. Thus, based on the received RF signals, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a supermarket shelf. The RFID reader periodically emits RF signals to identify all tags and the tags simply respond with their IDs via EPCglobal Class 1 Generation 2 protocol. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90 percent with up to ten human objects).
引用
收藏
页码:1054 / 1067
页数:14
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