Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data

International Conference on Computer Vision (ICCV) 2013, Sydney, Australia


Tracking the articulated 3D motion of the hand has important applications, for example, in human--computer interaction and teleoperation. We present a novel method that can capture a broad range of articulated hand motions at interactive rates. Our hybrid approach combines, in a voting scheme, a discriminative, part-based pose retrieval method with a generative pose estimation method based on local optimization. Color information from a multi-view RGB camera setup along with a person-specific hand model are used by the generative method to find the pose that best explains the observed images. In parallel, our discriminative pose estimation method uses fingertips detected on depth data to estimate a complete or partial pose of the hand by adopting a part-based pose retrieval strategy. This part-based strategy helps reduce the search space drastically in comparison to a global pose retrieval strategy. Quantitative results show that our method achieves state-of-the-art accuracy on challenging sequences and a near-realtime performance of 10 fps on a desktop computer.



BibTeX, 1 KB

author = {Sridhar, Srinath and Oulasvirta, Antti and Theobalt, Christian},
title = {Interactive Markerless Articulated Hand Motion Tracking using RGB and Depth Data},
booktitle = {Proceedings of the {IEEE} International Conference on Computer Vision ({ICCV})},
url = {},
numpages = {8},
month = Dec,
year = {2013}

Related Pages

  • HandSonor: A Customizable Vision-based Control Interface for Musical Expression, Extended Abstracts, CHI 2013 (webpage)


This work was supported by the Max Planck Center for Visual Computing and Communication (MPC-VCC) and the ERC Starting Grant CapReal. We would like to thank Han Du for helping us with image annotation and James Tompkin for helping with the supplementary video voice-over.


Srinath Sridhar

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