Perceiving the slightest tag motion beyond localization

Lei Yang, Yi Guo, Tianci Liu, Cheng Wang, Yunhao Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

Existing methods in RFID systems often employ presence or absence fashion to detect the tags' motions, so they cannot meet motion detection requirement in many applications. Our recent observations suggest that the signal strength backscattered from the tag is hypersensitive to its position, inspiring us to perceive the tag motion through its radio signal strength changes. Motion perception is not trivial and challenged by weak stability of strength in that any other interference or noise may incur significant changes as well, resulting in high false positives. To tackle this issue, we propose to model the strength via the Mixture of Gaussian Model (MoG). The problem is thus converted to foreground segment in computer vision with the help of Strength Image, where the technique of MoG based background subtraction is employed. We then implement a prototype using commercial off-the-shelf products. The evaluation results show that the slightest tag motion (10 cm) can be precisely perceived, and the accuracy is up to 92.34 percent while the false positive is suppressed under 0.5 percent.
Original languageEnglish
Article number7014237
Pages (from-to)2363-2375
Number of pages13
JournalIEEE Transactions on Mobile Computing
Volume14
Issue number11
DOIs
Publication statusPublished - 1 Nov 2015
Externally publishedYes

Keywords

  • Background substraction
  • Mixture of Gaussian model
  • Motion detection
  • RFID

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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