research-article

Identifying people in camera networks using wearable accelerometers

Authors:

Thiago Teixeira,

Gershon Dublon,

Andreas SavvidesAuthors Info & Claims

PETRA '09: Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 20, Pages 1 - 8

https://doi.org/10.1145/1579114.1579134

Published: 09 June 2009 Publication History

Abstract

We propose a system to identify people in a sensor network. The system fuses motion information measured from wearable accelerometer nodes with motion traces of each person detected by a camera node. This allows people to be uniquely identified with the IDs the accelerometer-node that they wear, while their positions are measured using the cameras. The system can run in real time, with high precision and recall results. A prototype implementation using iMote2s with camera boards and wearable TI EZ430 nodes with accelerometer sensorboards is also described.

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Cited By

Papaioannou SMarkham ATrigoni N(2017)Tracking People in Highly Dynamic Industrial EnvironmentsIEEE Transactions on Mobile Computing10.1109/TMC.2016.261352316:8(2351-2365)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1109/TMC.2016.2613523
Yang LWang WZhang QXing G(2016)VibIDProceedings of the 15th International Conference on Information Processing in Sensor Networks10.5555/2959355.2959366(1-12)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.5555/2959355.2959366
Papaioannou SWen HXiao ZMarkham ATrigoni NSong JAbdelzaher TMascolo C(2015)Accurate Positioning via Cross-Modality TrainingProceedings of the 13th ACM Conference on Embedded Networked Sensor Systems10.1145/2809695.2809712(239-251)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1145/2809695.2809712
Show More Cited By

Index Terms

Identifying people in camera networks using wearable accelerometers
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
        Vision for robotics

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cover image ACM Other conferences

PETRA '09: Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments

June 2009

481 pages

ISBN:9781605584096

DOI:10.1145/1579114

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 June 2009

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Division of Computer and Network Systems

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PETRA '09

PETRA '09: The 2nd International Conference on PErvasive Technologies Related to Assistive Environments

June 9 - 13, 2009

Corfu, Greece

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Cited By

Papaioannou SMarkham ATrigoni N(2017)Tracking People in Highly Dynamic Industrial EnvironmentsIEEE Transactions on Mobile Computing10.1109/TMC.2016.261352316:8(2351-2365)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1109/TMC.2016.2613523
Yang LWang WZhang QXing G(2016)VibIDProceedings of the 15th International Conference on Information Processing in Sensor Networks10.5555/2959355.2959366(1-12)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.5555/2959355.2959366
Papaioannou SWen HXiao ZMarkham ATrigoni NSong JAbdelzaher TMascolo C(2015)Accurate Positioning via Cross-Modality TrainingProceedings of the 13th ACM Conference on Embedded Networked Sensor Systems10.1145/2809695.2809712(239-251)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1145/2809695.2809712
Liu YWang QChen J(2015)Identification of People in a Camera's Field of View Using Acoustic Signal from Mobile Phone2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)10.1109/IMCCC.2015.176(805-810)Online publication date: Sep-2015
https://doi.org/10.1109/IMCCC.2015.176
Papaioannou SWen HMarkham ATrigoni N(2014)Fusion of Radio and Camera Sensor Data for Accurate Indoor PositioningProceedings of the 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems10.1109/MASS.2014.52(109-117)Online publication date: 28-Oct-2014
https://dl.acm.org/doi/10.1109/MASS.2014.52
Lee DHwang IOh S(2014)OPTIMUSMachine Vision and Applications10.1007/s00138-014-0607-425:4(901-917)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1007/s00138-014-0607-4
Lee DOh S(2013)Understanding human-place interaction from tracking and identification of many users2013 IEEE 1st International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA)10.1109/CPSNA.2013.6614256(112-115)Online publication date: Aug-2013
https://doi.org/10.1109/CPSNA.2013.6614256
Ferdous SVyas KMakedon FMakedon F(2012)A survey on multi person identification and localizationProceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2413097.2413143(1-3)Online publication date: 6-Jun-2012
https://dl.acm.org/doi/10.1145/2413097.2413143
Wang QLiu YChen J(2012)Accurate indoor tracking using a mobile phone and non-overlapping camera sensor networks2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings10.1109/I2MTC.2012.6229261(2022-2027)Online publication date: May-2012
https://doi.org/10.1109/I2MTC.2012.6229261
Greenstein BLongstaff BLamarca ACox L(2011)FollowMeProceedings of the 12th Workshop on Mobile Computing Systems and Applications10.1145/2184489.2184511(105-110)Online publication date: 1-Mar-2011
https://dl.acm.org/doi/10.1145/2184489.2184511
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