Video Event Recognition by Dempster-Shafer Theory

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

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
31 Downloads (Pure)

Abstract

This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high-level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.
Original languageEnglish
Title of host publication21 European Conference on Artificial Intelligence (ECAI 2014)
PublisherIOS Press
Pages1031-1032
Number of pages2
ISBN (Electronic)9781614994190
ISBN (Print)9781614994183
DOIs
Publication statusPublished - 1 Aug 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
NumberECAI 2014
Volume263
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Structured keywords

  • Jean Golding

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