An Expectation Maximisation Algorithm for Behaviour Analysis in Video

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 de- scribes 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.
Type de document :
Communication dans un congrès
Int. Conf. on Information Fusion (FUSION), Jul 2015, Washington D.C., United States. 2015
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https://hal-imt.archives-ouvertes.fr/hal-01198415
Contributeur : François Septier <>
Soumis le : samedi 12 septembre 2015 - 15:34:24
Dernière modification le : jeudi 11 janvier 2018 - 06:27:22

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  • HAL Id : hal-01198415, version 1

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Olga Isupova, Lyudmila Mihaylova, Danil Kuzin, Garik Markarian, François Septier. An Expectation Maximisation Algorithm for Behaviour Analysis in Video. Int. Conf. on Information Fusion (FUSION), Jul 2015, Washington D.C., United States. 2015. 〈hal-01198415〉

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