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

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3 Author(s)
Surachai Ongkittikul ; Sch. of Electron. & Phys. Sci., Univ. of Surrey, Guildford ; Stewart Worrall ; Ahmet Kondoz

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.

Published in:

Computer Information Systems and Industrial Management Applications, 2008. CISIM '08. 7th

Date of Conference:

26-28 June 2008