Classifying Surveillance Events from Attributes and Behaviour

In order to develop a high-level description of events unfolding in a typical surveillance scenario, each successfully tracked event must be classified into type and behaviour. In common with a number of approaches this paper employs a Bayesian classifier to determine type from event attribute such as height, width and velocity. The classifier, however, is extended to integrate all available evidence from the entire track. A not untypical Hidden Markov Model approach has been employed to model the common event behaviours typical of a car-park environment. Both techniques have been probabilistically integrated to generate accurate type and behaviour classifications.