Improved object tracking algorithm based on particle filter and Galerkin's method
Lei Guo · Journal of Computer Applications · 2011
In the particle filter framework,estimation accuracy strongly depends on the choice of proposal distribution.The traditional particle filter uses system transition probability as the proposal distribution without considering the new observing information;therefore,they cannot give accurate estimation.A new tracking framework applied with particle filter algorithm was proposed,which used Galerkin's method to construct proposal distribution.This proposal distribution enhanced the estimation accuracy compared to traditional filters.In the proposed framework,color model and shape model were adaptively fused,and a new model update scheme was also proposed to improve the stability of the object tracking.The experimental results demonstrate the availability of the proposed algorithm.