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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Pruteanu-Malinici, I; Carin, L

Published Date

  • January 1, 2007

Published In

Volume / Issue

  • 5 /

International Standard Serial Number (ISSN)

  • 1522-4880

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2007.4379784

Citation Source

  • Scopus