Detecting Abnormal Events via Hierarchical Dirichlet Processes
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Abnormality detection using low-level co-occurring events
Pattern Recognition Letters
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Multi-granularity video unusual event detection based on infinite Hidden Markov models
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
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Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Proceedings of the 20th ACM international conference on Multimedia
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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 ldquonormalrdquo/ldquotypicalrdquo 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.