Robust Maximum Margin Correlation Tracking

Abstract

Recent decade has seen great interest in the use of discriminative classifiers for tracking. Most trackers, however, focus on correct classification between the target and background. Though it achieves good generalization performance, the highest score of the classifier may not correspond to the correct location of the object. And this will produce localization error. In this paper, we propose an online Maximum Margin Correlation Tracker (MMCT) which combines the design principle of Support Vector Machine (SVM) and the adaptive Correlation Filter (CF). In principle, bipartite classifier SVM is designed to offer good generalization, rather than accurate localization. In contrast, CF can provide accurate target location, but it is not explicitly designed to offer good generalization. Through incorporating SVM with CF, MMCT demonstrates good generalization as well as accurate localization. And because the appearance can be learned in Fourier domain, the computational burden is reduced significantly. Extensive experiments on public benchmark sequences have proven the superior performance of MMCT over many state-of-the-art tracking algorithms.