Abstract
A partitional and a fuzzy clustering algorithm are compared in this paper in terms of accuracy, robustness and efficiency. 3D position data extracted from a stereo-vision system have to be clustered to use them in a tracking application in which a particle filter is the kernel of the estimation task. ‘K-Means’ and ‘Subtractive’ algorithms have been modified and enriched with a validation process in order improve its functionality in the tracking system. Comparisons and conclusions of the clustering results both in a stand-alone process and in the proposed tracking task are shown in the paper.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(nº 3), 264–323 (1999)
Berkhin, P.: Survey of clustering data mining techniques. Technical Report (2002)
Everitt, B., Landau, S., Leese, M.: Cluster analysis. Edward Arnold Publishers, 4th Edition, London (2001), ISBN: 0-340-76119-9
Marrón, M., Sotelo, M.A., García, J.C., Fernández, D., Pizarro, D.: XPFCP: An extended particle filter for tracking multiple and dynamic objects in complex environments. In: Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), Edmonton, pp. 234–239 (2005), ISBN: 0-7803-9252-3
Isard, M., Blake, C.A.: Conditional density propagation for visual tracking. International Journal of Computer Vision 29(nº1), 5–28 (1998)
Bar-Shalom, Y., Fortmann, T.: Tracking and data association. Mathematics in Science and Engineering, vol. 182. Academic Press, London (1988)
Schulz, D., Burgard, W., Fox, D., Cremers, A.B.: People tracking with mobile robots using sample-based joint probabilistic data association filters. International Journal of Robotics Research 22 (nº 2), 99–116 (2003)
Koller-Meier, E.B., Ade, F.: Tracking multiple objects using a condensation algorithm. Journal of Robotics and Autonomous Systems 34, 93–105 (2001)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, vol. 1, pp. 281–297 (1967)
Pelleg, D., Moore, A.: X-means: Extending k-means with efficient estimation of the number of clusters. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), San Francisco, pp. 727–734 (2000), ISBN: 1-55860-707-2
Weiss, Y.: Belief propagation and revision in networks with loops. Technical Report 1616, Artificial Intelligence Laboratory, Massachusetts Institute of Technology (1997)
Yager, R.R., Filev, D.: Generation of fuzzy rules by mountain clustering. Journal of Intelligent and Fuzzy Systems 2(nº 3), 209–219 (1994)
Chiu, S.L.: Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems 2(nº 3), 267–278 (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marrón Romera, M., Sotelo Vázquez, M.A., García García, J.C. (2007). Comparing Improved Versions of ‘K-Means’ and ‘Subtractive’ Clustering in a Tracking Application. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol 4739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75867-9_90
Download citation
DOI: https://doi.org/10.1007/978-3-540-75867-9_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75866-2
Online ISBN: 978-3-540-75867-9
eBook Packages: Computer ScienceComputer Science (R0)