A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of an entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm.
Reference:
Dabrowski, J.J. and De Villiers, J.P. 2015. A unified model for context-based behavioural modelling and classification. Expert Systems with Applications, 42(19), pp 6738–6757
Dabrowski, J., & De Villiers, J. P. (2015). A unified model for context-based behavioural modelling and classification. http://hdl.handle.net/10204/9416
Dabrowski, JJ, and Johan P De Villiers "A unified model for context-based behavioural modelling and classification." (2015) http://hdl.handle.net/10204/9416
Dabrowski J, De Villiers JP. A unified model for context-based behavioural modelling and classification. 2015; http://hdl.handle.net/10204/9416.
Copyright: 2015 Elsevier. This is a post-print version. The definitive version of the work is published in Expert Systems with Applications, 42(19), pp 6738–6757
