An expectation maximisation algorithm for behaviour analysis in video

Olga Isupova, Lyudmila Mihaylova, Danil Kuzin, Garik Markarian, Francois Septier

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

3 Citations (Scopus)

Abstract

Surveillance systems require advanced algorithms able to make decisions without a human operator or with minimal assistance from human operators. In this paper we propose a novel approach for dynamic topic modeling to detect abnormal behaviour in video sequences. The topic model describes activities and behaviours in the scene assuming behaviour temporal dynamics. The new inference scheme based on an Expectation-Maximisation algorithm is implemented without an approximation at intermediate stages. The proposed approach for behaviour analysis is compared with a Gibbs sampling inference scheme. The experiments both on synthetic and real data show that the model, based on Expectation-Maximisation approach, outperforms the one, based on Gibbs sampling scheme.

Original languageEnglish
Title of host publication2015 18th International Conference on Information Fusion, Fusion 2015
PublisherIEEE
Pages126-133
Number of pages8
ISBN (Electronic)978-0-9824-4386-6
Publication statusPublished - 17 Sep 2015
Event18th International Conference on Information Fusion, Fusion 2015 - Washington, USA United States
Duration: 6 Jul 20159 Jul 2015

Publication series

Name2015 18th International Conference on Information Fusion, Fusion 2015

Conference

Conference18th International Conference on Information Fusion, Fusion 2015
CountryUSA United States
CityWashington
Period6/07/159/07/15

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