Year: 2019
Type: article
Abstract: 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.
Source: IET Image Processing
Authors: Mohammadreza Javanmardi, Xiaojun Qi
Institution: Utah State University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2964099610