A Multiplicative Model of Appearance for Visual Tracking

In this paper we introduce a multiplicative, low dimensional model for appearance change in the context of visual tracking. The model is defined by a linear combination of smoothly changing basis functions. Secondly, we show that a small number of cluster centers in this new space can be used to represent the illumination conditions existing in the scene. These cluster centers can be learned from ground truth and are shown to generalize well to other objects of the same class for the scene. Finally we show how this illumination model can be combined with shape in a probabilistic sampling framework to improve tracking robustness under challenging conditions.