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


Conference Paper


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.

Author(s): Dimitrios Tzionas and Abhilash Srikantha and Pablo Aponte and Juergen Gall
Book Title: German Conference on Pattern Recognition (GCPR)
Pages: 1-13
Year: 2014
Month: September
Series: Lecture Notes in Computer Science
Publisher: Springer

Department(s): Perceiving Systems
Research Project(s): Hands in Action
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1007/978-3-319-11752-2_22
Event Name: GCPR 2014

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  title = {Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points},
  author = {Tzionas, Dimitrios and Srikantha, Abhilash and Aponte, Pablo and Gall, Juergen},
  booktitle = {German Conference on Pattern Recognition (GCPR)},
  pages = {1-13},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  month = sep,
  year = {2014},
  month_numeric = {9}