3D Hand Tracking

Overview |  References | Links | Acknowledgments | Contact

The software, developed in CVRLICSFORTH,  tracks the 3D position, orientation and full articulation of a human hand from markerless visual observations. The developed method

  • estimates the full articulation of a hand (26 DoFs)  involved in unconstrained motion
  • operates on input acquired by easy-to-install and widely used/supported RGB-D cameras (e.g. Kinect, Xtion)
  • does not require markers, special gloves
  • performs at a rate of 30fps in modern architectures (GPU acceleration)
  • does not require calibration
  • does not rely on any proprietary built-in tracking technologies (Nite, OpenNI, Kinect SDK)
Tracking a single hand with a Kinect
Tracking two hands with a Kinect

Strict system requirements:

  • PC with at least 1 GB of RAM
  • 64bit Windows OS or Linux OS
  • CUDA enabled GPU card (Compute Capability 1.0 and newer) with 256 ΜΒ of RAM

Also, if you are interested in performing a live demo, make sure that you have installed the x64 version of your RGB-D camera driver.

For a short introduction over the usage of the demo please consult the following video.

Sample recorded sequences are provided for testing, in addition to the one already included in the installation package. The presented videos regard 3D hand tracking with a computational budget of 64 particles and 30 generations. Additional sequences (data only) can be downloaded here: oni_sequences.zip.

Sequence 1

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Sequence 2

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Sequence 3

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Sequence 4

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Sequence 5

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This work got the 1st prize at the CHALEARN Gesture Recognition demonstration competition (Check also this link). The competition was organized in conjunction with ICPR 2012 (Tsukuba, Japan, Nov. 2012).

References

  • I. Oikonomidis, N. Kyriazis, and A. Argyros, “Efficient model-based 3D tracking of hand articulations using Kinect,” in BMVC 2011, 2011.
    [Bibtex]
    @inproceedings{bmvc2011oikonom,
      title={Efficient model-based 3D tracking of hand articulations using Kinect},
      author={Oikonomidis, I. and Kyriazis, N. and Argyros, A.},
      booktitle={BMVC 2011},
      pages={},
      year={2011},
      publisher={BMVA},
      description = { web-site of A. A. Argyros, web-site of N. Kyriazis },
      file = { paper, poster }
    }
  • N. Kyriazis, I. Oikonomidis, and A. Argyros, “A GPU-powered computational framework for efficient 3D model-based vision,” ICS-FORTH, TR420, , 2011.
    [Bibtex]
    @techreport{kyriazisTR420,
      title = {A GPU-powered computational framework for efficient 3D model-based vision},
      author = {Kyriazis, N. and Oikonomidis, I. and Argyros, A. },
      institution = {ICS-FORTH},
      year = {2011},
      month ={July},
      number = {TR420},
      description = { web-site of N. Kyriazis },
      file = { paper, poster }
    }

Links

Acknowledgments

  • The contributions of Damien Michel, Pashalis Padeleris and Konstantinos Tzevanidis, members of CVRL/ICS/FORTH, are gratefully acknowledged
  • This work was partially supported by the IST-FP7-IP-215821 project GRASP. Extensions of this work (in progress) are supported by the IST-FP7-IP-288533 project robohow.cog
  • GRASP and robohow.cog are projects funded by the European Commission through the Cognition unit, Information Society and Media DG

Contact

For questions, comments and any kind of feedback please pos an issue on our github page.