Tracking of Linear Appearance Models Using Second Order Minimization

摘要

The visual tracking of image regions is a research area of great interest within the computer vision community. One issue which has received quite attention in the last years has been the analysis of tracking algorithms which could be able to cope with changes in the appearance of the target region. Probably one of the most studied techniques proposed to model this appearance variability is that based on linear subspace models. Recently, efficient algorithms for fitting these models have been developed too, in many cases as an evolution of well studied approaches for the tracking of fixed appearance images.rnAdditionally, new methods based on second order optimizers have been proposed for the tracking of targets with no appearance changes. In this paper we study the application of such techniques in the design of tracking algorithms for linear appearance models and compare their performance with three previous approaches. The achieved results show the efficiency of the use of second-order minimization in terms of both number of iterations required for convergence and convergence frequency.