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An expectation maximisation algorithm for behaviour analysis in video

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Publication date15/07/2015
Host publicationInformation Fusion (Fusion), 2015 18th International Conference on
PublisherIEEE
Pages126-133
Number of pages8
ISBN (Print)9781479974047
Original languageEnglish
EventInternational Conference on Information Fusion'2015 - Washington DC, Washington DC USA, United States
Duration: 4/07/20158/07/2015

Conference

ConferenceInternational Conference on Information Fusion'2015
CountryUnited States
CityWashington DC USA
Period4/07/158/07/15

Conference

ConferenceInternational Conference on Information Fusion'2015
CountryUnited States
CityWashington DC USA
Period4/07/158/07/15

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.