Modelling events is one of the key problems in dynamic scene understanding when salient and autonomous visual changes occurring in a scene need to be characterised as a set of different object temporal events. we propose an approach to understand complex outdoor scenarios which is based on modelling temporally correlated events using Dynamic Bayesian Networks (DBNs). A Partially Coupled Hidden Markov Model (PCHMM) is exploited whose topology is determined automatically using Bayesian Information Criterion (BIC). Causality discovery and events modelling are also tackled using a Multi-Observation Hidden Markov Model (MOHMM).