Evidence reasoning for event inference in smart transport video surveillance

Xin Hong, WenJun Ma, Yan Huang, Paul Miller, Weiru Liu, Huiyu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
323 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationICDSC '14 Proceedings of the International Conference on Distributed Smart Cameras
DOIs
Publication statusPublished - 04 Nov 2014
EventACM/IEEE International Conference on Distributed Smart Cameras - Venezia, Italy
Duration: 04 Nov 201407 Nov 2014

Conference

ConferenceACM/IEEE International Conference on Distributed Smart Cameras
CountryItaly
CityVenezia
Period04/11/201407/11/2014

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