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A Study of Hand Motion/Posture Recognition in Two-Camera Views

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

This paper presents a vision-based approach for hand gesture recognition which combines both trajectory recognition and hand posture recognition. With two calibrated cameras, the 3D hand motion trajectory can be reconstructed. The reconstructed trajectory is then modeled by dynamic movement primitives (DMP) and a support vector machine (SVM) is trained to recognize five classes of gestures trajectories. Scale-invariant feature transform (SIFT) is used to extract features on segmented hand postures taken from both camera views. Based on various hand appearances captured by the two cameras, the proposed hand posture recognition method has shown a very good success rate. A gesture vector is proposed to combine the recognition result from both trajectory and hand postures. For our experimental set-up, it was shown that it is possible to accomplish a good overall accuracy for gesture recognition.

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Author information

Authors and Affiliations

  1. Experimental Robotics and Imaging Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

    Jingya Wang & Shahram Payandeh

Authors
  1. Jingya Wang
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  2. Shahram Payandeh
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Corresponding author

Correspondence to Shahram Payandeh .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Wang, J., Payandeh, S. (2015). A Study of Hand Motion/Posture Recognition in Two-Camera Views. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_29

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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