Discriminative level set for contour tracking

Abstract

Conventional contour tracking algorithms with level set often use generative models to construct the energy function. For tracking through cluttered and noisy background, however, a generative model may not be discriminative enough. In this paper we integrate the discriminative methods into a level set framework when constructing the level set energy function. We train a set of weak classifiers to distinguish the object from the background. Each weak classifier is designed to select the most discriminative feature space and integrated via AdaBoost according to their training errors. We also introduce a novel interaction term to explore the correlation between pixels near the object edge. This term together with the discriminative model both enhance the discriminative power of the level set. The experimental results show that the contour tracked by our approach is more accurate than the conventional algorithms with the generative model. Our algorithm successfully tracks the object contour even in a cluttered environment. © 2010 IEEE.