Adaptive gating in gaussian bayesian multi-target tracking

Abstract

Bayesian target tracking methods consist of filtering successive measurements coming from a detector. Linear and nonlinear Gaussian Bayesian filters are well adapted to estimate the successive a posteriori state distributions of a single moving target from a sequence of observations. However, when tracking several targets in a cluttered environment, previous techniques must be combined with dedicated procedures for validating and associating the measurements to their predictions. Gating validation techniques are used to increase the reliability of the association technique by retaining only the measurements that could be originated from predicted measurements. In standard techniques, the only constraint imposed on the gate is to contain the correct measurement. However, as the shape of the validation gate is related to the covariance of the transition noise, it is of major importance to estimate it in a reliable manner. We therefore review several methods to update the covariance of transition noise and we propose a new one that enables the validation gate to be adapted both to the smoothly evolving dynamic of a moving target and to an abruptly changing dynamic. All the methods are compared for performance on microscopy image sequences which typically contain objects that abruptly change their behavior.