Abstract
3D motion capture is a form of multimedia data that is widely used in animation and medical fields (such as physical medicine and rehabilitation where body joint analysis is needed). These applications typically create large repositories of motion capture data and need efficient and accurate content-based retrieval techniques. 3D motion capture data is in the form of multi-dimensional time series data. To reduce the dimensions of human motion data while maintaining semantically important features, we quantize human motion data by extracting Spatial-Temporal Features through SVD and translate them onto a 1-dimensional sequential representation through our proposed sGMMEM (semantic Gaussian Mixture Modeling with EM). Thus, we achieve good classification accuracies for primitive human motion categories (walking 92.85%,run 91.42%,jump 94.11%) and even for subtle categories (dance 89.47%,laugh 83.33%,basketball signal 85.71%,golf putting 80.00%).
This is a preview of subscription content, log in via an institution to check access.
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
Li, C., Kulkarni, P.R., Prabhakaran, B.: Motion Stream Segmentation and Recognition by Classification. International Journal of Multimedia Tools and Applications (MTAP) by Springer-Verlag 35(1) (2007)
Li, C., Pradhan, G., Zheng, S.Q., Prabhakaran, B.: Indexing of Variable Length Multi-attribute Motion data. In: Proc. of the Second ACM International Workshop on Multimedia, Washington D.C., USA, pp. 75–84 (November 2004)
CMU Motion Capture Library, http://mocap.cs.cmu.edu/
Pradhan, G.N., Li, C., Prabhakaran, B.: Hierarchical Indexing Structure for 3D Human Motion. In: Int’l Proceedings of ACM Multimedia Modeling Conference (MMM) 2007, Singapore, January 9-12 (2007)
Muller, M., Roder, T., Clausen, M.: Efficient content based retrieval of motion capture data. ACM Transactions on Graphics (TOG) 24, 677–685 (2005)
Liu, G., Zhang, J., Wang, W., McMillan, L.: A system for analyzing and indexing human-motion databases. In: Proc. 2005 ACM SIGMOD International conference on Management of data (2005)
Ketterer, J., Puzicha, J., Held, M.: On Spatial Quantization of Color Images. IEEE Transactions on Image Processing 9, 666–682 (2000)
Liu, F., Zhuang, Y., Wu, F., Pan, Y.: 3D motion retrieval with motion index tree. Computer Vision and Image Understanding 92, 265–284 (2003)
Keogh, E., Palpanas, T., Zordan, V.B., Gunopulos, D., Cardle, M.: Indexing large human-motion databases. In: Proc. 30th VLDB Conference, Toronto, Canada, pp. 780–791 (2004)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society B39, 1–38
Golub, G.H., Loan, C.F.: Matrix Computations. The Johns Hopkins University Press, Baltimore, Maryland (1996)
Kohonen, T., Kangas, J., Laaksonen, J., Torkkola, K.: A program package for the correct application of Learning Vector Quantization algorithms. In: Proceedings of the International Joint Conference on Neural Networks, Baltimore, pp. 725–730 (June 1992)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jin, Y., Prabhakaran, B. (2008). Semantic Quantization of 3D Human Motion Capture Data Through Spatial-Temporal Feature Extraction. In: Satoh, S., Nack, F., Etoh, M. (eds) Advances in Multimedia Modeling. MMM 2008. Lecture Notes in Computer Science, vol 4903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77409-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-77409-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77407-5
Online ISBN: 978-3-540-77409-9
eBook Packages: Computer ScienceComputer Science (R0)