Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays

被引：37

作者：

Liu, Yunhao ^{[1
]}

Chen, Lei ^{[1
]}

Pei, Jian ^{[2
]}

Chen, Qiuxia ^{[1
]}

Zhao, Yiyang ^{[1
]}

机构：

[1] Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China

[2] Simon Fraser Univ, Burnaby, BC V5A 1S6, Canada

来源：

FIFTH ANNUAL IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS, PROCEEDINGS | 2007年

基金：

中国博士后科学基金; 加拿大自然科学与工程研究理事会;

关键词：

D O I：

10.1109/PERCOM.2007.23

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Activity monitoring, a crucial task in many applications, is often conducted expensively using video cameras. Also, effectively monitoring a large field by analyzing images from multiple cameras remains a challenging problem. In this paper, we introduce a novel application of the recently developed RFID technology: using RF tag arrays for activity monitoring, where data mining techniques play a critical role. Pie RFID technology provides an economically attractive solution due to the low cost Of RF tags and readers. Another novelty of this design is that the tracking objects do not need to attach any transmitters or receivers, such as tags or readers. By developing a practical fault-tolerant method, we offset the noise of RF tag data and mine ftequent trajectory patterns as models of regular activities. Our empirical study using real RFID systems and data sets verifies the feasibility and the effectiveness of our design.

引用

页码：37 / +

页数：2