In this paper, a localized particle subset method is proposed to solve target tracking problem in a Bayesian inference framework. Instead of using all particles to estimated the posterior probability density function (pdf) of targets, a subset is used. This subset of particles is selected by estimated motion of the targets. The weights of particles are updated by the 3D Hausdroff distances between target appearance model and samples. The proposed method is highly efficient in utilizing the particles, which consequently results in reduction of samples utilized in the prediction and update processes. It is also able to alleviate the sample degeneracy and impoverishment problems in the sampling process. Experiments show that the computation complexity for localized particle subset tracker is reduce to a fraction of that of the Sequential Importance (SIS) tracker but with compatible performance.