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Multi-view Head Detection and Tracking with Long Range Capability for Social Navigation Planning

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Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6939))

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Abstract

Head pose is one of the important human cues in social navigation planning for robots to coexist with humans. Inferring such information from distant targets using a mobile platform is a challenging task. This paper tackles this issue to propose a method for detecting and tracking head pose with the mentioned constraints using RGBD camera (Kinect, Microsoft). Initially possible human regions are segmented out then validated by using depth and Hu moment features. Next, plausible head regions within the segmented areas are estimated by employing Haar-like features with the Adaboost classifier. Finally, the obtained head regions are post-validated by means of their dimension and their probability of containing skin before refining the pose estimation and tracking by a boosted-based particle filter. Experimental results demonstrate the feasibility of the proposed approach for detecting and tracking head pose from far range targets under spot-light and natural illumination conditions.

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Author information

Authors and Affiliations

  1. Graduate School of Science & Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, Saitama, 338-8570, Japan

    Razali Tomari, Yoshinori Kobayashi & Yoshinori Kuno

Authors
  1. Razali Tomari
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  2. Yoshinori Kobayashi
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  3. Yoshinori Kuno
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Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, West Lafayette, 47907-2021, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana at Lafayette, 70504, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

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© 2011 Springer-Verlag Berlin Heidelberg

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Tomari, R., Kobayashi, Y., Kuno, Y. (2011). Multi-view Head Detection and Tracking with Long Range Capability for Social Navigation Planning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_42

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  • DOI: https://doi.org/10.1007/978-3-642-24031-7_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24030-0

  • Online ISBN: 978-3-642-24031-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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