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Markerless and
Efficient 26-DOF Hand Pose Recovery |
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Brief description We present a
novel method that, given a sequence of synchronized views of a human hand,
recovers its 3D position, orientation and full articulation parameters. The
adopted hand model is based on properly selected and assembled 3D geometric
primitives. Hypothesized configurations/poses of the hand model are projected
to different camera views and image features such as edge maps and hand
silhouettes are computed. An objective function is then used to quantify the
discrepancy between the predicted and the actual, observed features. The
recovery of the 3D hand pose amounts to estimating the parameters that
minimize this objective function which is performed using Particle Swarm
Optimization. All the basic components of the method (feature extraction,
objective function evaluation, optimization process) are inherently parallel.
Thus, a GPU-based implementation achieves a speedup of two orders of
magnitude over the case of CPU processing. Extensive experimental results
demonstrate qualitatively and quantitatively that accurate 3D pose recovery
of a hand can be achieved robustly at a rate that greatly outperforms the
current state of the art. ·
You might also
be interested in having a look at our more work on efficient
model-based 3D tracking of hand articulations using Kinect (BMVC’2011) where
instead of exploiting 2D visual cues extracted by a multicamera setup, we
employ 2D and 3D visual cues resulting from a Kinect (RGB-D) sensor. A more recent extension considers tracking the
articulated motion of two strongly interacting hands (CVPR 2012). ·
You might also
be interested in having a look at our more recent work on full DOF tracking
of a hand interacting with an object by modeling occlusions and physical
constraints (ICCV'2011) where we do not only seek for the optimal hand
model that explains the available hand observations alone, but rather for the
joint hand-object model that best explains both the available hand/object
observations and the occlusions. Sample results
Performance
of the proposed method for different values of selected parameters. In the
plots of the top row, the vertical axis represents the mean value E of the
optimization function. In the plots of the bottom row, the vertical axis
represents mean error in mm (see paper for additional details). (a), (b):
Varying values of PSO particles and generations for 2 views. (c), (d): Same
as (a),(b) but for 8 views. (e),(f):
Increasing number of views. (g), (h): Increasing amounts of noise.
Number
of multiframes per second processed for a number of PSO generations and
camera views for 16/128 particles per generation. The entry in boldface corresponds
to 20 generations, 16 particles per generation and 2 views. This setup
corresponds to the best trade-off between accuracy of results, computational
performance and system complexity. This figure shows that the proposed method
is capable of accurately and efficiently recovering the 3D pose of a hand
observed from a stereo camera configuration at 6.2Hz. If 8 cameras are
employed, the method delivers poses at a rate of 1.6Hz.
Download video with 3D hand
pose estimation results. The blue contours in the above snapshot (right) and the
accompanying video are the projections of the 3D hand model (left) as this
has been estimated by the proposed method for these sequences. Contributors Iasonas Oikonomidis, Nikolaos Kyriazis, Antonis
Argyros. This
work was partially supported by the IST-FP7-IP-215821 project GRASP. Relevant publications ·
I. Oikonomidis, N. Kyriazis and A.A. Argyros, “Markerless and
Efficient 26-DOF Hand Pose Recovery”, in Proceedings of the 10th Asian
Conference on Computer Vision, ACCV’2010, Part III , LNCS 6494, pp. 744–757,
Queenstown, New Zealand, Nov. 8-12, 2010. ·
I. Oikonomidis, N. Kyriazis and A.A. Argyros, “Full DOF tracking of a
hand interacting with an object by modeling occlusions and physical
constraints”, in Proceedings of the
13th IEEE International Conference on Computer Vision, ICCV’2011, Barcelona,
Spain, Nov. 6-13, 2011. ·
I. Oikonomidis, N. Kyriazis and A.A. Argyros, “Efficient model-based
3D tracking of hand articulations using Kinect”, in Proceedings of the 22nd British Machine
Vision Conference, BMVC’2011, University of Dundee, UK, Aug. 29-Sep. 1, 2011. ·
I. Oikonomidis, N. Kyriazis and A.A. Argyros, “Tracking the
articulated motion of two strongly interacting hands”, to appear in the
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,
CVPR 2012, Rhode Island, USA, June 18-20, 2012. ·
N. Kyriazis, I. Oikonomidis,
A.A. Argyros, “A GPU-powered computational framework for efficient 3D
model-based vision”, Technical Report TR420, Jul. 2011, ICS-FORTH, 2011. The
electronic versions of the above publications can be downloaded from my publications page. |
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Last update: |
03 March 2012, Antonis Argyros, argyros@ics.forth.gr |
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