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A Graphical Model for unifying tracking and classification within a multimodal Human-Robot Interaction scenario

Tobias Rehrl, Jurgen Gast, Nikolaus Theissing, Alexander Bannat, Dejan Arsic, Frank Wallhoff, Gerhard Rigoll, Christoph Mayer, Bernd Radig
2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops  

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This paper introduces our research platform for enabling a multimodal Human-Robot Interaction scenario as well as our research vision: approaching problems in a holistic way to realize this scenario. However, in this paper the main focus is laid on the image processing domain, where our vision has been realized by combining particle tracking and Dynamic Bayesian Network classification in a unified Graphical Model. This combination allows for enhancing the tracking process by an adaptive motion
more » ... odel realized via a Dynamic Bayesian Network modeling several motion classes. The Graphical Model provides a direct integration of the classification step in the tracking process. First promising results show the potential of the approach. 1 978-1-4244-7028-0/10/$26.00 ©2010 IEEE
doi:10.1109/cvprw.2010.5543751 dblp:conf/cvpr/RehrlGTBAWRMR10 fatcat:xyuqfibvx5ag3k5mhnkqpcltqq
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Tobias Rehrl, Jurgen Gast, Nikolaus Theissing, Alexander Bannat, Dejan Arsic, Frank Wallhoff, Gerhard Rigoll, Christoph Mayer, Bernd Radig. "A Graphical Model for unifying tracking and classification within a multimodal Human-Robot Interaction scenario." 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops (2010) 17-23