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

Tobias Rehrl; Jürgen Gast; Nikolaus Theißing; Alexander Bannat; Dejan Arsić; Frank Wallhoff; Gerhard Rigoll; Christoph Mayer; Bernd Radig

doi:10.1109/CVPRW.2010.5543751

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

Tobias Rehrl, Jürgen Gast, Nikolaus Theißing, Alexander Bannat, Dejan Arsić, Frank Wallhoff, Gerhard Rigoll, Christoph Mayer, Bernd Radig

Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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 model 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.

Original language	English
Title of host publication	2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
Pages	17-23
Number of pages	7
DOIs	https://doi.org/10.1109/CVPRW.2010.5543751
State	Published - 2010
Event	2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 - San Francisco, CA, United States Duration: 13 Jun 2010 → 18 Jun 2010

Publication series

Name	2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

Conference

Conference	2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
Country/Territory	United States
City	San Francisco, CA
Period	13/06/10 → 18/06/10

Access to Document

10.1109/CVPRW.2010.5543751

Cite this

Rehrl, T., Gast, J., Theißing, N., Bannat, A., Arsić, D., Wallhoff, F., Rigoll, G., Mayer, C., & Radig, B. (2010). A Graphical Model for unifying tracking and classification within a multimodal Human-Robot Interaction scenario. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 (pp. 17-23). Article 5543751 (2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010). https://doi.org/10.1109/CVPRW.2010.5543751

Rehrl, Tobias ; Gast, Jürgen ; Theißing, Nikolaus et al. / 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, CVPRW 2010. 2010. pp. 17-23 (2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010).

@inproceedings{eb9558b1df484b54bfbc6ba41d1e8625,

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

abstract = "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 model 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.",

author = "Tobias Rehrl and J{\"u}rgen Gast and Nikolaus Thei{\ss}ing and Alexander Bannat and Dejan Arsi{\'c} and Frank Wallhoff and Gerhard Rigoll and Christoph Mayer and Bernd Radig",

year = "2010",

doi = "10.1109/CVPRW.2010.5543751",

language = "English",

isbn = "9781424470297",

series = "2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010",

pages = "17--23",

booktitle = "2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010",

note = "2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 ; Conference date: 13-06-2010 Through 18-06-2010",

}

Rehrl, T, Gast, J, Theißing, N, Bannat, A, Arsić, D, Wallhoff, F, Rigoll, G, Mayer, C & Radig, B 2010, A Graphical Model for unifying tracking and classification within a multimodal Human-Robot Interaction scenario. in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010., 5543751, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, pp. 17-23, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, San Francisco, CA, United States, 13/06/10. https://doi.org/10.1109/CVPRW.2010.5543751

A Graphical Model for unifying tracking and classification within a multimodal Human-Robot Interaction scenario. / Rehrl, Tobias; Gast, Jürgen; Theißing, Nikolaus et al.
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010. 2010. p. 17-23 5543751 (2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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 model 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.

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Rehrl T, Gast J, Theißing N, Bannat A, Arsić D, Wallhoff F et al. A Graphical Model for unifying tracking and classification within a multimodal Human-Robot Interaction scenario. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010. 2010. p. 17-23. 5543751. (2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010). doi: 10.1109/CVPRW.2010.5543751

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

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