Abnormal Behaviour Detection in Video using Topic Modeling

Olga Isupova, Danil Kuzin, Lyudmila Mihaylova

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

Abstract

The growth of the number of surveillance systems makes it is impossible to process data by human operators thereby autonomous algorithms are required in a decision-making procedure. A novel dynamic topic modeling approach for abnormal behaviour detection in video is proposed. Activities and behaviours in the scene are described by the topic model where temporal dynamics for behaviours is assumed. Here we implement Expectation-Maximisation algorithm for inference in the model and show in the experiments that it outperforms the Gibbs sampling inference scheme that is originally proposed in [1].
Original languageEnglish
Title of host publication The University of Sheffield Engineering Symposium
Number of pages2
DOIs
Publication statusPublished - 2015
EventThe University of Sheffield Engineering Symposium - Sheffield, UK United Kingdom
Duration: 24 Jun 201524 Jun 2015

Other

OtherThe University of Sheffield Engineering Symposium
Abbreviated titleUSES 2015
CountryUK United Kingdom
CitySheffield
Period24/06/1524/06/15

Keywords

  • Abnormal behaviour
  • Computer vision
  • Expectation-Maximisation algorithm
  • Gibbs sampling
  • Topic modeling

Cite this

Isupova, O., Kuzin, D., & Mihaylova, L. (2015). Abnormal Behaviour Detection in Video using Topic Modeling. In The University of Sheffield Engineering Symposium https://doi.org/10.15445/02012015.18