  Sparse representation has recently been successfully applied in visual
tracking. It utilizes a set of templates to represent target candidates and
find the best one with the minimum reconstruction error as the tracking result.
In this paper, we propose a robust deep features-based structured group local
sparse tracker (DF-SGLST), which exploits the deep features of local patches
inside target candidates and represents them by a set of templates in the
particle filter framework. Unlike the conventional local sparse trackers, the
proposed optimization model in DF-SGLST employs a group-sparsity regularization
term to seamlessly adopt local and spatial information of the target candidates
and attain the spatial layout structure among them. To solve the optimization
model, we propose an efficient and fast numerical algorithm that consists of
two subproblems with the closed-form solutions. Different evaluations in terms
of success and precision on the benchmarks of challenging image sequences
(e.g., OTB50 and OTB100) demonstrate the superior performance of the proposed
tracker against several state-of-the-art trackers.
