access icon free Structured group local sparse tracker

© The Institution of Engineering and Technology
Received 30/11/2018, Accepted 01/04/2019, Revised 08/03/2019, Published 10/04/2019

Sparse representation is considered as a viable solution to visual tracking. In this study, the authors proposed a structured group local sparse tracker (SGLST), which exploits the local patches inside target candidates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimisation 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 regularisation term. To solve the optimisation model, the authors proposed 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.

Inspec keywords: image sequences; optimisation; image representation; image reconstruction; particle filtering (numerical methods); object tracking

Other keywords: sparse representation; optimisation model; local information; structured group local sparse tracker; spatial information; local patches; target candidates; conventional local sparse trackers; spatial layout structure; group-sparsity regularisation term

Subjects: Computer vision and image processing techniques; Interpolation and function approximation (numerical analysis); Optical, image and video signal processing

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