Abstract 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).
DBNs, partially coupled HMM and multiobservation HMM are exploited in REF for causality discovery and events modelling.
Discovering Bayesian causality among visual events in a complex outdoor scene
Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.
Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.
Computer Science