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abstract

3D auto-calibration method for head-mounted binocular gaze tracker as human-robot interface

Published: 03 March 2013 Publication History

Abstract

This paper presents a novel calibration method for a head-mounted binocular gaze tracker that enables the human gaze point, representing the selective visual attention of the user, to be tracked in 3D space. The proposed method utilizes two calibration planes with visual marks to calculate the mapping points between a forward-looking camera and two eye-monitoring cameras in an expanded 3D spatial domain. As a result, the visually attentive point of the user can be tracked, regardless of variations in the distance from the user to the target object. The proposed method also provides a more convenient calibration procedure and more accurate results in tests than the previous method suggested by the authors. The performance is tested when varying the 3D position of an attentive object, and the experimental results are discussed.

References

[1]
M. Y. Kim, S. Yang, and D. Kim, "Head-Mounted Binocular Gaze Detection for Selective Visual Recognition Systems," Sensors and Actuators A: Physical, Vol. 187, pp. 29--36, Nov. 2012.
[2]
D. Li, "Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches," Proc. of 2005 IEEE Computer Vision and Pattern Recognition, pp. 79, 2005.
[3]
Z. Zhiwei and J. Qiang, "Novel Eye Gaze Tracking Techniques Under Natural Head Movement," IEEE Trans. Biomedical Engineering, Vol. 54, No. 12, pp. 2246--2260, 2007.

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    cover image ACM Conferences
    HRI '13: Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
    March 2013
    452 pages
    ISBN:9781467330558

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    • AAAI: American Association for Artificial Intelligence
    • Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc

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    IEEE Press

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    Published: 03 March 2013

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    Author Tags

    1. binocular gaze
    2. calibration
    3. gaze tracker

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

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