BMVC 2004, Kingston, 7th-9th Sept, 2004
Robust Fusion of Colour Appearance Models for Object Tracking
C. Town and S. Moran (University of Cambridge)
This paper reports on work which fuses three different appearance models to
enable robust tracking of multiple objects on the basis of colour. Short-term
variation in object colour is modelled non-parametrically using adaptive binning
histograms. Appearance changes at intermediate time scales are represented
by semi-parametric (Gaussian mixture) models while a parametric
subspace method (Robust PCA) is employed to model long term stable appearance.
Fusion of the three models is achieved through particle filtering
and the Democratic integration method. It is shown how robust estimation
and adaptation of the models both individually and in combination results in
improved visual tracking accuracy.