RFCamera: Identifying RFIDs in Pixel Dimensions

Qiongzheng Lin, Lei Yang, Zhenlin An, Yi Guo, Ping Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Radio Frequency IDentification (RFID) is emerging as a vital technology of the Internet of Things. Billions of RFID tags have been deployed to locate daily objects such as equipment, pharmaceuticals, and vehicles, and so on. Unlike previous solutions that focus on localizing tagged objects in the world coordinate system in reference to reader antennas, this work exploits a system, called RFCamera, that can identify and locate RFID-tagged objects in images with pixel dimensions. Many applications would benefit from RFCamera. For instance, the RF-aware image annotation system is able to generate rich annotations for RFID-tagged entities in images at the pixel level for the deep learning; the RF-aware auto-focus allows surveillance camera to exactly focalize the burglar who carries the stolen tagged-property out of a crowd. Our core insight is that an image is a visual AoA profile in terms of lights, which is resulted from the pinhole camera model. Similarly, we generate an RF image, derived from the AoA profile of a tag using the same pinhole model as the camera. Consequently, the locations of visual entities corresponding to tagged objects are highlighted by comparing two types of images. To this end, we customized a camera system equipped with a pair of rotatable reader antennas. Our experimental evaluation demonstrates that RFCamera enables a mean error of 5.7° and 2.9° at azimuth and elevation angle estimation. It can locate a visual entity with a mean error of 51 pixels (i.e., ≈ 1.3 cm at 96 dpi) in a 640 × 480 image.

Original languageEnglish
Title of host publication2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
PublisherIEEE Computer Society
Pages1-9
Number of pages9
ISBN (Electronic)9781728166308
DOIs
Publication statusPublished - Jun 2020
Event17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020 - Virtual, Online, Italy
Duration: 22 Jun 202025 Jun 2020

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
Country/TerritoryItaly
CityVirtual, Online
Period22/06/2025/06/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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