Relations to improve multi-target tracking in an activity recognition system

The explicit recognition of the relationships between interacting objects can improve the understanding of their dynamics. In this work, we investigate the use of relational dynamic Bayesian networks to represent the interactions between moving objects in a surveillance system. We use a transition model that incorporates first-order logic relations and a two-phases particle filter algorithm in order to directly track relations between targets. We present some results about activity recognition in monitoring coastal borders.