NDLI: Mobile targets region-of-interest via distributed pyroelectric sensor network: Towards a robust, real-time context reasoning

Content Provider	IEEE Xplore Digital Library
Author	Fei Hu Qingquan Sun Qi Hao
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, 35487, USA (Fei Hu; Qingquan Sun; Qi Hao)
Abstract	We have established a multi-walker recognition / tracking testbed based on low-cost pyroelectrc sensor network (PSN). In order to identify a region of interest (RoI) in the monitoring area for the detection of any interesting mobile targets, we propose to use Bayesian machine learning and binary signal projection to extract the statistical contextual features from real-time, high-dimensional PSN data. This paper describes our recent results in this area, which include two aspects: (1) we have proposed to use binary principle component analysis (B-PCA) to interpret the relationship between observed sensor data and hidden context patterns. (2) We have conducted comprehensive experiments from real PSN sensor data to verify the context detection accuracy based on B-PCA models. Our results show that B-PCA can better capture context basis than general PCA algorithm $^{1}.$
Starting Page	1832
Ending Page	1836
File Size	777083
Page Count	5
File Format	PDF
ISBN	9781424481705
ISSN	19300395
e-ISBN	9781424481699
e-ISBN	9781424481682
DOI	10.1109/ICSENS.2010.5690006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Principal component analysis Data mining Mobile communication Databases Target tracking Equations
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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