Visual tracking with online discriminative learning

Abstract

We treat tracking as a binary classification task in order to distinguish between an object to be tracked and the background. We propose to integrate an online learning based total-error-rate minimization method (OTER) with an observation model of particle filter for visual tracking. The particle filter is modeled using an affine dynamic model and an observation model. The observation model is constructed using the OTER classifier for binary pattern classification. The proposed method is empirically evaluated both qualitatively and quantitatively using several publicly available video sequences.