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Two Hand Tracking Using Colour Statistical Model with the K-means Embedded Particle Filter for Hand Gesture Recognition

Ongkittikul, S, Worrall, S and Kondoz, A Two Hand Tracking Using Colour Statistical Model with the K-means Embedded Particle Filter for Hand Gesture Recognition

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Abstract

Particle filtering is an efficient and successful technique for tracking 2D and 3D motion through an image. We present the enhanced tracking of two hands based on a statistical model using only a skin colour feature with particle filtering for gesture recognition. Our framework employs one particle filter per hand individually with the pixel-wise classification of the likelihood of the skin in the window search. The skin classifier decision was trained from a set of skin samples in YCrCb space using an elliptical model. The tracking scheme employs a reliability measurement derived from the particle distribution which is used to adaptively weight the colour classification. The K-means algorithm is used to discriminate the split and merge between left and right hand. Experiments with a set of videos including the movement of two hands in cluttered backgrounds show that adaptive use of our scheme provides improvement compared to use with other techniques such as mean-shift tracking.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Ongkittikul, SUNSPECIFIEDUNSPECIFIED
Worrall, Ss.worrall@surrey.ac.ukUNSPECIFIED
Kondoz, Aa.kondoz@surrey.ac.ukUNSPECIFIED
Identification Number : https://doi.org/10.1109/CISIM.2008.19
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 11:37
Last Modified : 17 May 2017 14:57
URI: http://epubs.surrey.ac.uk/id/eprint/832067

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