  Sparse representation is considered as a viable solution to visual tracking.
In this paper, we propose a structured group local sparse tracker (SGLST),
which exploits local patches inside target candidates in the particle filter
framework. Unlike the conventional local sparse trackers, the proposed
optimization model in SGLST not only adopts local and spatial information of
the target candidates but also attains the spatial layout structure among them
by employing a group-sparsity regularization term. To solve the optimization
model, we propose an efficient numerical algorithm consisting of two
subproblems with the closed-form solutions. Both qualitative and quantitative
evaluations on the benchmarks of challenging image sequences demonstrate the
superior performance of the proposed tracker against several state-of-the-art
trackers.
