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Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons85110

Tzionas,  Dimitris
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons85108

Srikantha,  Abhilash
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Tzionas, D., Srikantha, A., Aponte, P., & Gall, J. (2014). Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points. In Pattern Recognition. 36th German Conference, GCPR 2014. Proceedings (pp. 277-289). Cham et al.: Springer International Publishing.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-E35D-9
Abstract
Hand motion capture has been an active research topic in recent years, following the success of full-body pose tracking. Despite similarities, hand tracking proves to be more challenging, characterized by a higher dimensionality, severe occlusions and self-similarity between fingers. For this reason, most approaches rely on strong assumptions, like hands in isolation or expensive multi-camera systems, that limit the practical use. In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera. Our approach combines a generative model with collision detection and discriminatively learned salient points. We quantitatively evaluate our approach on 14 new sequences with challenging interactions.