Discovering Bayesian causality among visual events in a complex outdoor scene

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 the Bayesian information criterion (BIC). Causality discovery and events modelling are also tackled using a multi-observation hidden Markov model (MOHMM).