Comparing improved versions of 'K-means' and 'subtractive' clustering in a tracking application

Authors:
Marta Marrón Romera;Miguel Angel Sotelo Vázquez;Juan Carlos García García
Affiliations:
Electronics Department, University of Alcala, Edificio Politécnico, Alcalá de Henares, Madrid, Spain;Electronics Department, University of Alcala, Edificio Politécnico, Alcalá de Henares, Madrid, Spain;Electronics Department, University of Alcala, Edificio Politécnico, Alcalá de Henares, Madrid, Spain
Venue:
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Year:
2007

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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.