University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Enhanced hand tracking using the k-means embedded particle filter with mean-shift vector re-sampling

Ongkittikul, S, Worrall, S and Kondoz, A (2008) Enhanced hand tracking using the k-means embedded particle filter with mean-shift vector re-sampling

Full text not available from this repository.

Abstract

Particle filters have been applied with great success to 2D and 3D tracking problems. We presents the tracking of two hands based on a statistical model using only a skin colour feature with particle filtering for gesture recognition. The tracking scheme employs the reliability measurement derived from the particle distribution which is used to adaptively weight the skin-pixel colour classification. Our approach chooses shift-vectors to re-weight the particle sample to improve accuracy and reduce the number of samples. The k-means algorithm is used to discriminate the split and merge between left and right hands in case they are close together. Experiments with a set of videos including the movement of two hands in sample and cluttered backgrounds show that adaptive use of our scheme provides improvement compared to use with auxiliary particle filter of the number of samples and accuracy. ©2008 The Institution of Engineering and Technology.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Ongkittikul, SUNSPECIFIEDUNSPECIFIED
Worrall, Ss.worrall@surrey.ac.ukUNSPECIFIED
Kondoz, Aa.kondoz@surrey.ac.ukUNSPECIFIED
Date : 1 December 2008
Identification Number : https://doi.org/10.1049/cp:20080277
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 11:20
Last Modified : 17 May 2017 14:55
URI: http://epubs.surrey.ac.uk/id/eprint/830994

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800