NDLI: Structured group local sparse tracker

Content Provider	IET Digital Library
Author	Javanmardi, Mohammadreza Qi, Xiaojun
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.
Starting Page	1391
Ending Page	1399
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 8, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6578
Journal	IET Image Processing
Publisher Date	2019-04-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Conventional Local Sparse Trackers Group-sparsity Regularisation Term Image Reconstruction Image Representation Image Sequence Interpolation And Function Approximation Local Information Local Patches Numerical Analysis Object Tracking Optical, Image And Video Signal Processing Optimisation Optimisation Model Particle Filter Sparse Representation Spatial Information Spatial Layout Structure Structured Group Local Sparse Tracker Target Candidates
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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