Robust Fusion of Colour Appearance Models for Object Tracking
Christopher P. Town, Seán Moran · 2004
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. 1 Introduction and Related Work Appearance models play a vital role in visual tracking of objects. They must remain robust to confounding factors such as noise, occlusions, lighting changes, and background variation while adapting to appearance changes caused by motions and deformations of