Temporal Bayesian Networks for Scenario Recognition

Abstract

Abstract. This work presents an automatic scenario recognition system for video sequence interpretation. The recognition algorithm is based on a Bayesian Networks approach. The model of scenario contains two main layers. The first one enables to highlight atemporal events from the observed visual features. The second layer is focused on the temporal reasoning stage. The temporal layer integrates an event based approach in the framework of the Bayesian Networks. The temporal Bayesian network tracks lifespan of relevant events highlighted from the first layer. Then it estimates qualitative and quantitative relations between temporal events helpful for the recognition task. The global recognition algorithm is illustrated over real indoor images sequences for an abandoned baggage scenario.