NDLI: Object tracking using compressive local appearance model with ℓ1-regularisation

Content Provider	IET Digital Library
Author	Kim, Hyuncheol Paik, Joonki
Abstract	A novel compressive local appearance model-based object tracking algorithm is presented to address challenging issues in object tracking. To efficiently preserve image patches of an object and reduce the dimensionality, a random projection-based feature selection method is introduced. Modelling the object's appearance using a sparse representation over a set of templates leads to an ℓ1-regularisation problem. To solve this problem, both the reconstruction error and the residual matrix are considered which play a key role in tracking an object with severe appearance variations using the modified likelihood function. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods in terms of dealing with long-term partial occlusion, deformation and rotation.
Starting Page	444
Ending Page	446
Page Count	3
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 6, Mar (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2013.2763
Journal	Electronics Letters
Publisher Date	2014-03-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Computer Vision And Image Processing Technique Dimensionality Reduction Feature Selection Image Patches Image Representation Matrix Algebra Modified Likelihood Function Novel Compressive Local Appearance Model-based Object Tracking Algorithm Object Tracking Optical, Image And Video Signal Processing Random Projection-based Feature Selection Method Reconstruction Error Residual Matrix Sparse Representation Video Signal Processing Â„“1-regularisation Problem
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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