Abstract
The representation of human movements for recognition and synthesis is important in many application fields such as: surveillance, human-computer interaction, motion capture, and humanoid robots. Hidden Markov models (HMMs) are a common statistical framework in this context, since they are generative and are able to deal with the intrinsic dynamic variation of movements performed by humans. In this work we argue that many human movements are parametric, i.e., a parametric variation of the movements in dependence of, e.g., a position a person is pointing at. The parameter is part of the semantic of a movement. And while classic HMMs treat them as noise, we will use parametric HMMs (PHMMs) [6,19] to model the parametric variability of human movements explicitly. In this work, we discuss both types of PHMMs, as introduced in [6] and [19], and we will focus our considerations on the recognition and synthesis of human arm movements. Furthermore, we will show in various experiments the use of PHMMs for the control of a humanoid robot by synthesizing movements for relocating objects at arbitrary positions. In vision-based interaction experiments, PHMM are used for the recognition of pointing movements, where the recognized parameterization conveys to a robot the important information which object to relocate and where to put it. Finally, we evaluate the accuracy of recognition and synthesis for pointing and grasping arm movements and discuss that the precision of the synthesis is within the natural uncertainty of human movements.
This work was partially funded by PACO-PLUS (IST-FP6-IP-027657).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Asfour, T., Welke, K., Ude, A., Azad, P., Hoeft, J., Dillmann, R.: Perceiving objects and movements to generate actions on a humanoid robot. In: Proc. Workshop: From features to actions – Unifying perspectives in computational and robot vision, ICRA, Rome, Italy (April 2007)
Dariush, B.: Human Motion Analysis for Biomechanics and Biomedicine. Machine Vision and Applications 14, 202–205 (2003)
Grest, D., Woetzel, J., Koch, R.: Nonlinear Body Pose Estimation from Depth Images. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 285–292. Springer, Heidelberg (2005)
Gunter, S., Bunke, H.: Optimizing the number of states, training iterations and Gaussians in an HMM-based handwritten word recognizer. In: Proc. Seventh International Conference on Document Analysis and Recognition, vol. 01, pp. 472–476 (2003)
Herzog, D., Grest, D., Krueger, V.: An Online Recognition Demo, http://www.cvmi.aau.dk/~deh/demo/recognition.avi
Herzog, D., Krüger, V., Grest, D.: Parametric Hidden Markov Models for Recognition and Synthesis of Movements. In: Proceedings of the British Machine Vision Conference (BMVC), Leeds, UK, September 2008, vol. 1, pp. 163–172 (2008)
Herzog, D., Ude, A., Krueger, V.: HOAP-3 Online Demo, http://www.cvmi.aau.dk/~deh/demo/robot.avi
Herzog, D., Ude, A., Krueger, V.: Motion Imitation and Recognition using Parametric Hidden Markov Models. In: Proc. 8th IEEE-RAS International Conference on Humanoid Robots Humanoids 2008, December 2008, pp. 339–346 (2008)
Huang, X., Ariki, Y., Jack, M.: Hidden Markov Models for Speech Recognition. Edinburgh University Press (1990)
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G.: Adaptive mixtures of local experts. Neural Computation 3, 79–87 (1991)
Keogh, E., Pazzani, M.: Derivative dynamic time warping. In: Proceedings of the 1st SIAM International Conference on Data Mining (SDM 2001), Chicago, USA (2001)
Lu, C., Ferrier, N.: Repetitive Motion Analysis: Segmentation and Event Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 258–263 (2004)
Moeslund, T., Hilton, A., Krueger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104(2-3), 90–127 (2006)
Murphy, K.P.: Hidden semi-Markov models (HSMMs). In: unpublished notes (2002)
Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. In: IEEE ASSP Magazine, January 1986, pp. 4–15 (1986)
Ramana, P.K.R., Grest, D., Krüger, V.: Human Action Recognition in Table-top Scenarios: An HMM-based Analysis to Optimize the Performance. In: Proceedings of Computer Analysis of Images and Patterns, Vienna, pp. 101–108 (2007)
Schaal, S.: Is Imitation Learning the Route to Humanoid Robots? Trends in Cognitive Sciences 3(6), 233–242 (1999)
Vecchio, D., Murray, R., Perona, P.: Decomposition of Human Motion into Dynamics-based Primitives with Application to Drawing Tasks. Automatica 39(12), 2085–2098 (2003)
Wilson, A.D., Bobick, A.F.: Parametric hidden Markov models for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9), 884–900 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Herzog, D., Krüger, V. (2009). Recognition and Synthesis of Human Movements by Parametric HMMs. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds) Statistical and Geometrical Approaches to Visual Motion Analysis. Lecture Notes in Computer Science, vol 5604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03061-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-03061-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03060-4
Online ISBN: 978-3-642-03061-1
eBook Packages: Computer ScienceComputer Science (R0)