Visual tracking using particle filters with Gaussian process regression

Abstract

Particle degeneracy is one of the main problems when particle filters are applied to visual tracking. The effective solution methods on the degeneracy phenomenon include good choice of proposal distribution and use of resampling. In this paper, we propose a novel visual-tracking algorithm using particle filters with Gaussian process regression and resampling techniques, which effectively abate the influence of particle degeneracy and improve the robustness of visual tracking. The main characteristic of the proposed algorithm is that we incorporate particle filters with Gaussian process regression which can learn highly effective proposal distributions for particle filters to track the visual objects. Experimental results in challenging sequences demonstrate the effectiveness and robustness of the proposed method. © 2009 Springer Berlin Heidelberg.