Improved particle filters for multi-target tracking

Authors:
Vasileios Maroulas;Panos Stinis
Affiliations:
Department of Mathematics, University of Tennessee, Knoxville, TN 37996, United States;Department of Mathematics, University of Minnesota, Minneapolis, MN 55455, United States
Venue:
Journal of Computational Physics
Year:
2012

Citing 7
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Abstract

We present a novel approach for improving particle filters for multi-target tracking. The suggested approach is based on drift homotopy for stochastic differential equations. Drift homotopy is used to design a Markov Chain Monte Carlo step which is appended to the particle filter and aims to bring the particle filter samples closer to the observations while at the same time respecting the target dynamics. We have used the proposed approach on the problem of multi-target tracking with a nonlinear observation model. The numerical results show that the suggested approach can improve significantly the performance of a particle filter.