Infinite Hidden Markov models for unusual-event detection in video

Iulian Pruteanu-Malinici, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


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. © 2008 IEEE.
Original languageEnglish (US)
Pages (from-to)811-822
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number5
StatePublished - May 1 2008
Externally publishedYes

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