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In this paper we present a new event recognition framework, based on the Dempster-Shafer theory of evidence, which combines the evidence from multiple atomic events detected by low-level computer vision analytics. The proposed framework employs evidential network modelling of composite events. This approach can effectively handle the uncertainty of the detected events, whilst inferring high-level events that have semantic meaning with high degrees of belief. Our scheme has been comprehensively evaluated against various scenarios that simulate passenger behaviour on public transport platforms such as buses and trains. The average accuracy rate of our method is 81% in comparison to 76% by a standard rule-based method.
|Title of host publication||ICDSC '14 Proceedings of the International Conference on Distributed Smart Cameras|
|Publication status||Published - 04 Nov 2014|
|Event||ACM/IEEE International Conference on Distributed Smart Cameras - Venezia, Italy|
Duration: 04 Nov 2014 → 07 Nov 2014
|Conference||ACM/IEEE International Conference on Distributed Smart Cameras|
|Period||04/11/2014 → 07/11/2014|