Dynamics modeling is of primal interest to track objects using particle filters. Even the choice of a well fitted noise parameter may lead to unsuccessful tracking when unexpected events arise, such as outliers, occultations, dynamics discontinuites… In this paper, we propose to introduce structural spatial information in particle filters. This information, expressed as spatial relations such as orientation or distance, is modeled in a fuzzy set framework, and is introduced in the dynamics in order to model the potential changes from one instant to the next one. The fuzzy modeling provides flexibility both in the semantics of the relations and in the transitions from one relation to another one. We show in our experiments that this kind of modeling is really adaptive to unexpected changes of dynamics, and outperforms classical filtering techniques while using only a small number of particles.