Video manifold modelling: finding the right parameter settings for anomaly detection

Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford

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

Abstract

Using video manifold to analyze video scenes and detect possible anomaly has become a popular research topic in recent years. While a number of attempts have been proposed and reported promising outcomes, there is currently a lack of understanding about the parameter setting for various components in the algorithmic framework. In this paper we look at some key parameters, particularly the dimension of the video manifold, the embedding dimension of the video trajectory, and explore the plausibility of setting these parameters automatically using outcome of spectral clustering and fractal dimension analysis. Experiments are conducted using a benchmark dataset and the results are promising.

Original languageEnglish
Title of host publicationIVCNZ '12
Subtitle of host publicationProceedings of the 27th Conference on Image and Vision Computing New Zealand
EditorsBrendan McCane, Steven Mills, Jeremiah Dreng
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages168-173
Number of pages6
ISBN (Print)978-1-4503-1473-2
DOIs
Publication statusPublished - 26 Nov 2012
Event27th Conference on Image and Vision Computing: New Zealand - Dunedin, New Zealand
Duration: 26 Nov 201228 Nov 2012

Conference

Conference27th Conference on Image and Vision Computing
Abbreviated titleIVCNZ 2012
Country/TerritoryNew Zealand
CityDunedin
Period26/11/1228/11/12

Keywords

  • anomaly detection
  • manifold learning
  • trajectory embedding

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