Comparing Improved Versions of 'K-Means' and 'Subtractive' Clustering in a Tracking Application

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.