Structured Group Local Sparse Tracker
This is an incremental improvement for visual tracking in computer vision, addressing the problem of maintaining spatial structure in target candidates.
The paper tackles visual tracking by proposing a structured group local sparse tracker (SGLST) that incorporates local patches, spatial information, and group-sparsity regularization to maintain target layout structure. It demonstrates superior performance against state-of-the-art trackers on challenging benchmarks through both qualitative and quantitative evaluations.
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.