Dabrowski, JJDe Villiers, JP2017-08-222017-08-222015-11Dabrowski, 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 6738-67570957-4174http://www.sciencedirect.com/science/article/pii/S0957417415003036http://hdl.handle.net/10204/9476Copyright: Elsevier Publishing. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website.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 a 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.enDynamic Bayesian networkSwitching linear dynamical systemInformation fusionBehaviour modellingActivity recognitionMaritime piracyArticleDabrowski, J., & De Villiers, J. (2015). A unified model for context-based behavioural modelling and classification. http://hdl.handle.net/10204/9476Dabrowski, JJ, and JP De Villiers "A unified model for context-based behavioural modelling and classification." (2015) http://hdl.handle.net/10204/9476Dabrowski J, De Villiers J. A unified model for context-based behavioural modelling and classification. 2015; http://hdl.handle.net/10204/9476.TY - Article AU - Dabrowski, JJ AU - De Villiers, JP AB - 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 a 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. DA - 2015-11 DB - ResearchSpace DP - CSIR KW - Dynamic Bayesian network KW - Switching linear dynamical system KW - Information fusion KW - Behaviour modelling KW - Activity recognition KW - Maritime piracy LK - https://researchspace.csir.co.za PY - 2015 SM - 0957-4174 T1 - A unified model for context-based behavioural modelling and classification TI - A unified model for context-based behavioural modelling and classification UR - http://hdl.handle.net/10204/9476 ER -