Abstract
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.
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Oikonomidis, I., Kyriazis, N., Argyros, A.A. (2011). Markerless and Efficient 26-DOF Hand Pose Recovery. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_58
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DOI: https://doi.org/10.1007/978-3-642-19318-7_58
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