Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient PointsDimitrios Tzionas and Abhilash Srikantha and Pablo Aponte and Juergen Gall
----> Extended IJCV version of the project here <----
(accepted on 10.02.2016)
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.
Tzionas, D., Srikantha, A., Aponte, P. and Gall, J.
Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points (PDF, BibTex)
German Conference on Pattern Recognition (GCPR'14)
Supplementary Material: Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points (PDF, Files)
Sequences marked with (*) are used just for comparison with the FORTH tracker.
Model-files marked with (**) do not contain sequence-specific files (.SKEL and .MOTION)