Infinite hidden Markov models and ISA features for unusual-event detection in video

Iulian Pruteanu-Malinici, Lawrence Carin

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

2 Scopus citations

Abstract

We address the problem of unusual-event detection in a video sequence. Invariant subspace analysis (ISA) is used to extract features from the video, and the time-evolving properties of these features are modeled via an infinite hidden Markov model (iHMM), which is trained using "normal"/ "typical" video data. The iHMM automatically determines the proper number of HMM states, and it retains a full posterior density function, on all model parameters. Anomalies (unusual events) are detected subsequently if a low likelihood is observed when associated sequential features are submitted to the trained iHMM. A hierarchical Dirichlet process (HDP) framework is employed in the formulation of the iHMM. The evaluation of posterior distributions for the iHMM is achieved in two ways: via MCMC and using a variational Bayes (VB) formulation. ©2007 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Image Processing, ICIP
PublisherIEEE Computer Societyhelp@computer.org
ISBN (Print)1424414377
DOIs
StatePublished - Jan 1 2007
Externally publishedYes

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