Drift-free tracking via the construction of an effective dictionary

Abstract

Template dictionary construction is an important issue in sparse representation (SP)-based tracking algorithms. In this article, a drift-free visual tracking algorithm is proposed via the construction of an effective template dictionary. The constructed dictionary is composed of three categories of atoms (templates): nonpolluted atoms, variational atoms, and noise atoms. Moreover, the linear combinations of nonpolluted atoms are also added to the dictionary for the diversity of atoms. All the atoms are selectively updated to capture appearance changes and alleviate the model drifting problem. A bidirectional tracking process is used and each process is optimized by two-step SP, which greatly reduces the computational burden. Compared with other related works, the constructed dictionary and tracking algorithm are both robust and efficient.