Unsupervised clustering of people from 'skeleton' data

Authors:
Adrian Ball;David Rye;Fabio Ramos;Mari Velonaki
Affiliations:
The University of Sydney, Sydney, Australia;The University of Sydney, Sydney, Australia;The University of Sydney, Sydney, Australia;The University of Sydney, Sydney, Australia
Venue:
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Year:
2012

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Abstract

This paper investigates the possibility of recognising individual persons from their walking gait using three-dimensional 'skeleton' data from an inexpensive consumer-level sensor, the Microsoft 'Kinect'. In an experimental pilot study it is shown that the K-means algorithm - as a candidate unsupervised clustering algorithm - is able to cluster gait samples from four persons with a nett accuracy of 43.6%.