TY - GEN

T1 - An expectation maximisation algorithm for behaviour analysis in video

AU - Isupova, Olga

AU - Mihaylova, Lyudmila Stoyanova

AU - Kuzin, Danil

AU - Markarian, Garegin

AU - Septier, Francois

PY - 2015/7/15

Y1 - 2015/7/15

N2 - 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.

AB - 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.

M3 - Conference contribution/Paper

SN - 9781479974047

SP - 126

EP - 133

BT - Information Fusion (Fusion), 2015 18th International Conference on

PB - IEEE

T2 - International Conference on Information Fusion'2015

Y2 - 4 July 2015 through 8 July 2015

ER -