Open AccessObject tracking using compressive local appearance model with ℓ 1 ‐regularisation
Author(s) -
Kim Hyuncheol,
Paik Joonki
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2013.2763
Subject(s) - artificial intelligence , active appearance model , computer vision , tracking (education) , video tracking , object (grammar) , computer science , random projection , pattern recognition (psychology) , residual , rotation (mathematics) , feature (linguistics) , sparse approximation , projection (relational algebra) , compressed sensing , mathematics , algorithm , image (mathematics) , psychology , pedagogy , linguistics , philosophy
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 ℓ 1 ‐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.