Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input

Srinath Sridhar, Franziska Mueller, Michael Zollhöfer, Dan Casas, Antti Oulasvirta, Christian Theobalt

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

58 Citations (Scopus)
185 Downloads (Pure)


Real-time simultaneous tracking of hands manipulating and interacting with external objects has many potential applications in augmented reality, tangible computing, and wearable computing. However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately. Many previous approaches resort to complex multi-camera setups to remedy the occlusion problem and often employ expensive segmentation and optimization steps which makes real-time tracking impossible. In this paper, we propose a real-time solution that uses a single commodity RGB-D camera. The core of our approach is a 3D articulated Gaussian mixture alignment strategy tailored to hand-object tracking that allows fast pose optimization. The alignment energy uses novel regularizers to address occlusions and hand-object contacts. For added robustness, we guide the optimization with discriminative part classification of the hand and segmentation of the object. We conducted extensive experiments on several existing datasets and introduce a new annotated hand-object dataset. Quantitative and qualitative results show the key advantages of our method: speed, accuracy, and robustness.
Original languageEnglish
Title of host publicationComputer Vision - ECCV 2016
Subtitle of host publication14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016 : proceedings, part 2
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Number of pages17
ISBN (Electronic)978-3-319-46475-6
Publication statusPublished - 1 Oct 2016
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Computer Vision - Amsterdam, Netherlands
Duration: 11 Oct 201614 Oct 2016
Conference number: 14

Publication series

ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Computer Vision
Abbreviated titleECCV

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