Abstract
Non-verbal interaction signals are of great interest in the research field of natural human-robot interaction. These signals are not limited to gestures and emotional expressions since other signals - like the interpersonal distance and orientation - do also have large influence on the communication process. Therefore, this paper presents a marker-less mono-ocular object pose estimation using a model-to-image registration technique. The object model uses different feature types and visibilities which allow the modeling of various objects. Final experiments with different feature types and tracked objects show the flexibility of the system. It turned out that the introduction of feature visibility allows pose estimations when only a subset of the modeled features is visible. This visibility is an extension to similar approaches found in literature.
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References
Birdwhistell, R.L.: Kinesics and Context: Essays in Body Motion Communication. University of Pennsylvania Press, Philadelphia (1970)
David, P., De Menthon, D., Duraiswami, R., Samet, H.: Softposit: Simultaneous pose and correspondence determination. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 698–714. Springer, Heidelberg (2002)
Dornaika, F., Orozco, J., Gonzà les, J.: Combined head, lips, eyebrows, and eyelids tracking using adaptive apperarence models. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2006. LNCS, vol. 4069, pp. 110–119. Springer, Heidelberg (2006)
Duncan, S., Fiske, D.W.: Face-To-Face Interaction. Lawrence Erlbaum Associates, Hillsdale (1977)
Grujić, N., Ilić, S., Lepetit, V., Fua, P.: 3d facial pose estimation by image retrieval. In: 8th IEEE Int’l Conference on Automatic Face and Gesture Recognition (2008)
Hall, E.T.: The hidden dimension. Anchor (1966)
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(4), 607–626 (2009)
Nistér, D., Stévenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2161–2168 (2006)
Sanderson, C., Palival, K.K.: Identity verification using speech and face information. In: Digital Signal Processing, pp. 449–480 (2004)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (June 1994)
Vatahska, T., Bennewitz, M., Behnke, S.: Feature-based head pose estimation from images. In: Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Humanoids (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, pp. 511–518 (2001)
Zhu, Y., Fujimura, K.: 3d head pose estimation with optical flow and depth constraints. In: Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling, pp. 211–216 (2003)
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Schmitz, N., Zolynski, G., Berns, K. (2010). Human Head Pose Estimation Using Multi-appearance Features. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_7
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DOI: https://doi.org/10.1007/978-3-642-16111-7_7
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