Tracking efficiency measurement of dynamic models on geometric particle filter using KLD-resampling

Abstract

Particle filter has appeared as a useful tool for visual object tracking. The efficiency of the particle filter depends mostly on the number of particles used in the estimation. This paper would like to measure the efficiency of particle filter via the Kullback-Leibler distance (KLD). The basis of the method is similar to Fox's KLD-sampling but implemented differently using resampling. The benefit of this approach is that the underlying distribution is exactly the posterior distribution. By means of batch KLD-resampling, we measure the efficiency of several dynamic models by calculating the average number of needed samples. Using experiments, we found (i) the efficiency of particle filter can be measure reliably enough using batch KLD-resampling, (ii) dynamics models affect the efficiency of particle filter, but their performance depends mostly on the case by case situationally.