Infinite hidden Markov models for unusual-event detection in video.
Journal Article (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. The iHMM retains a full posterior density function on all model parameters, including the number of underlying HMM states. 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 framework is employed in the formulation of the iHMM. The evaluation of posterior distributions for the iHMM is achieved in two ways: via Markov chain Monte Carlo and using a variational Bayes formulation. Comparisons are made to modeling based on conventional maximum-likelihood-based HMMs, as well as to Dirichlet-process-based Gaussian-mixture models.
Full Text
Duke Authors
Cited Authors
- Pruteanu-Malinici, I; Carin, L
Published Date
- May 2008
Published In
Volume / Issue
- 17 / 5
Start / End Page
- 811 - 822
PubMed ID
- 18390385
Electronic International Standard Serial Number (EISSN)
- 1941-0042
International Standard Serial Number (ISSN)
- 1057-7149
Digital Object Identifier (DOI)
- 10.1109/tip.2008.919359
Language
- eng