A Method to Adaptively Propagate the Set of Samples Used by Particle Filters

In recent years, particle filters have emerged as a useful tool that enables the application of Bayesian reasoning to problems requiring dynamic state estimation. The efficiency and accuracy of these type of filters are highly dependent on an appropriate propagation of the particles in time. In this paper we present a new method to improve the propagation step of the regular particle filter. Using results from the theory of importance sampling, our method adaptively propagates the set of samples without adding a significant computational load to the normal operation of the filter. Compared to existing techniques, our approach introduces two important enhancements: 1) An adaptive method to improve the propagation function, 2) A mechanism to identify when the use of adaptation is beneficial. We show the advantages of our method by applying the resulting filter to the visual tracking of targets in a real video sequence.