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2014 | OriginalPaper | Buchkapitel

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

verfasst von : Dimitrios Tzionas, Abhilash Srikantha, Pablo Aponte, Juergen Gall

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Hand motion capture has been an active research topic, 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 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.

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The annotated dataset sequences and the supplementary material are available at http://files.is.tue.mpg.de/dtzionas/GCPR_2014.html.

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Titel: Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points
verfasst von: Dimitrios Tzionas
Abhilash Srikantha
Pablo Aponte
Juergen Gall
Verlag: Springer International Publishing
Buch: Pattern Recognition
Print ISBN: 978-3-319-11751-5

Electronic ISBN: 978-3-319-11752-2

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-319-11752-2_22

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