Abstract
This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is measured with respect to precision and recall in a retrieval scenario, where the pattern is used as a motion query. Our experiments show that motion class patterns can automate query specification without loss of retrieval quality.
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
Brox, T., Rosenhahn, B., Cremers, D., Seidel, H.-P.: Nonparametric density estimation with adaptive anisotropic kernels for human motion tracking. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds.) Human Motion 2007. LNCS, vol. 4814, pp. 152–165. Springer, Heidelberg (2007)
Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. ACM Trans. Graph. 23(3), 559–568 (2004)
Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104(2), 90–126 (2006)
Müller, M.: Information Retrieval for Music and Motion. Springer, Heidelberg (2007)
Müller, M., Röder, T.: Motion templates for automatic classification and retrieval of motion capture data. In: Proc. ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 137–146. ACM Press, New York (2006)
Müller, M., Röder, T., Clausen, M.: Efficient content-based retrieval of motion capture data. ACM Trans. Graph. 24(3), 677–685 (2005)
Müller, M., Röder, T., Clausen, M., Eberhardt, B., Krüger, B., Weber, A.: Documentation: Mocap Database HDM05. Computer Graphics Technical Report CG-2007-2, Universität Bonn (2007)
Pohlheim, H.: Evolutionäre Algorithmen: Verfahren, Operatoren und Hinweise. Springer, Heidelberg (1999)
Rosenhahn, B., Klette, R., Metaxas, D.: Human Motion Understanding, Modeling, Capture, and Animation. Springer, Heidelberg (2007)
Sidenbladh, H., Black, M.J., Sigal, L.: Implicit probabilistic models of human motion for synthesis and tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 784–800. Springer, Heidelberg (2002)
Sminchisescu, C., Jepson, A.: Generative modeling for continuous non-linearly embedded visual inference. In: Proc. Int. Conf. on Machine Learning (2004)
Urtasun, R., Fleet, D.J., Fua, P.: 3D people tracking with Gaussian process dynamical models. In: Proc. International Conference on Computer Vision and Pattern Recognition, pp. 238–245. IEEE Computer Society Press, Los Alamitos (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Müller, M., Demuth, B., Rosenhahn, B. (2008). An Evolutionary Approach for Learning Motion Class Patterns. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-69321-5_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69320-8
Online ISBN: 978-3-540-69321-5
eBook Packages: Computer ScienceComputer Science (R0)