Object tracking using compressive local appearance model with l(1)-regularisation

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 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.