A Particle Filter to Track Multiple Objects

We address the problem of tracking multiple objects encountered in many situations in signal or image processing. We consider stochastic dynamic systems nonlinearly and uncompletely observed. The difficulty lies on the fact that the estimation of the states requires the assignation of the observations to the multiple targets. We propose an extension of the classical particle filter where the stochastic vector of assignation is estimated by a Gibbs sampler. The merit of the method is assessed in bearings-only context and we present one application in image-based tracking. 1