Infinite Hidden Markov Models and ISA Features for Unusual-Event Detection in Video

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.