Robust visual tracking with dual group structure

Abstract

The “sparse representation”-based tracking framework generally considers the testing candidates and dictionary atoms individually, thus failing to model the structured information within data. In this paper, we present a robust tracking framework by exploiting the dual group structure of both candidate samples and dictionary templates, and formulate the sparse representation at group level. The similar samples are encoded simultaneously by a few atom groups, which induces the inter-group sparsity, and also each group enjoys different internal sparsity. In this way, not only the potential commonality shared by the related candidates is taken into account, but also the individual differences between samples are reflected. Then we provide an effective optimization method to solve our formulation by two stages: thresholding and computing with the accelerated proximal gradient method. Finally, we embed the dual group structure model into the particle filter framework for visual tracking. Extensive experimental results demonstrate that our tracker achieves favorable performance against the state-of-the-art tracking methods.