Detecting interaction above digital tabletops using a single depth camera

Digital tabletop environments offer a huge potential to realize application scenarios where multiple users interact simultaneously or aim to solve collaborative tasks. So far, research in this field focuses on touch and tangible interaction, which only takes place on the tabletop’s surface. First ap...

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Bibliographic Details
Published in:Machine vision and applications 2013-11, Vol.24 (8), p.1575-1587
Main Authors: Haubner, Nadia, Schwanecke, Ulrich, Dörner, Ralf, Lehmann, Simon, Luderschmidt, Johannes
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Cameras
Communications Engineering
Computer Science
Computer science
control theory
systems
Computer simulation
Computer systems and distributed systems. User interface
Configurations
Exact sciences and technology
Image Processing and Computer Vision
Metaphor
Networks
Original Paper
Parameter estimation
Pattern Recognition
Pattern recognition. Digital image processing. Computational geometry
Segmentation
Segments
Simulated annealing
Software
Vision systems
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Summary:Digital tabletop environments offer a huge potential to realize application scenarios where multiple users interact simultaneously or aim to solve collaborative tasks. So far, research in this field focuses on touch and tangible interaction, which only takes place on the tabletop’s surface. First approaches aim at involving the space above the surface, e.g., by employing freehand gestures. However, these are either limited to specific scenarios or employ obtrusive tracking solutions. In this paper, we propose an approach to unobtrusively segment and detect interaction above a digital surface using a depth sensing camera. To achieve this, we adapt a previously presented approach that segments arms in depth data from a front-view to a top-view setup facilitating the detection of hand positions. Moreover, we propose a novel algorithm to merge segments and give a comparison to the original segmentation algorithm. Since the algorithm involves a large number of parameters, estimating the optimal configuration is necessary. To accomplish this, we describe a low effort approach to estimate the parameter configuration based on simulated annealing. An evaluation of our system to detect hands shows that a repositioning precision of approximately 1 cm is achieved. This accuracy is sufficient to reliably realize interaction metaphors above a surface.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-013-0538-5