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User recognition based on continuous monitoring and tracking

Published: 06 March 2011 Publication History

Abstract

This paper presents a user recognition system, using face, height, and clothes color features under the special assumption that is a user is monitored and tracked. In real human-robot interaction situation, all information cannot be provided at the same time and some parts of frames in a video have no clues at all. In the proposed system, tracking is an important feature to recognize a user because data in the previous frames can be utilized. We propose an information update method that efficiently updates similarity results. This system is tested using the movie clips acquired under the unconstrained environment including illumination variation, several distance from a camera to the user, and various view types of human body.

References

[1]
E. J. Ploran, et al., "Evidence Accumulation and the Moment of Recognition: Dissociating Perceptual Recognition Processes Using fMRI," The Journal of Neuroscience, Vol. 27, No. 44, pp.11912--11924, 2007
[2]
Wonjun Kim, Jaeho Lee, Minjin Kim, Daeyoung Oh, and Changick Kim, "Human Action Recognition Using Ordinal Measure of Accumulated Motion, "EURASIP Journal on Advances in Signal Processing Vol. 2010 pp.1--11, 2010
[3]
Manuel Lucena1, Nicolás Pérez de la Blanca2, José Manuel Fuertes, and Manuel Jesús Marín-Jiménez, "Human Action Recognition Using Optical Flow Accumulated Local Histograms," IbPRAI 2009, LNCS 5524, pp.32--39, 2009, Publiser, Springer-Verlag Berlin Heidelberg 2009.
[4]
M. E. Nilsback, B. Caputo, "Cue Integration through Discriminative Accumulation," CVPR'04, Vol. 2.
[5]
Maria-Elena Nilsback, "A Cue-Integration Scheme for Object Recognition Using Discriminative Accumulation," KTH, Sweden, 2004.
[6]
A. Pronobis, B. Caputo, "Confidence-based Cue Integration for Visual Place Recognition," Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.2394--2401, 2007

Cited By

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  • (2013)Reinforced AdaBoost Learning for Object Detection with Local Pattern RepresentationsThe Scientific World Journal10.1155/2013/1534652013:1Online publication date: 28-Nov-2013
  • (2013)Heterogeneous information network for person recognition2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)10.1109/URAI.2013.6677436(723-724)Online publication date: Oct-2013
  • (2013)Fuzzy-based intelligent control strategy for a person following robot2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)10.1109/ROBIO.2013.6739831(2408-2413)Online publication date: Dec-2013

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  1. User recognition based on continuous monitoring and tracking

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    Published In

    cover image ACM Conferences
    HRI '11: Proceedings of the 6th international conference on Human-robot interaction
    March 2011
    526 pages
    ISBN:9781450305617
    DOI:10.1145/1957656

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    • RA: IEEE Robotics and Automation Society
    • Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc
    • The Association for the Advancement of Artificial Intelligence (AAAI)
    • IEEE Systems, Man and Cybernetics Society

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 March 2011

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    Author Tags

    1. hri
    2. monitoring
    3. tracking
    4. update
    5. user recognition

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    Overall Acceptance Rate 268 of 1,124 submissions, 24%

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    View all
    • (2013)Reinforced AdaBoost Learning for Object Detection with Local Pattern RepresentationsThe Scientific World Journal10.1155/2013/1534652013:1Online publication date: 28-Nov-2013
    • (2013)Heterogeneous information network for person recognition2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)10.1109/URAI.2013.6677436(723-724)Online publication date: Oct-2013
    • (2013)Fuzzy-based intelligent control strategy for a person following robot2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)10.1109/ROBIO.2013.6739831(2408-2413)Online publication date: Dec-2013

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