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Clustering human behaviors with dynamic time warping and hidden Markov models for a video surveillance system

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2 Author(s)
Ouivirach, K. ; Comput. Sci. & Inf. Manage., Asian Inst. of Technol., Pathumthani, Thailand ; Dailey, M.N.

We propose and experimentally evaluate a new method for clustering human behaviors that is suitable for bootstrapping an anomaly detection module for intelligent video surveillance systems. The method uses dynamic time warping, agglomerative hierarchical clustering, and hidden Markov models to provide an initial partitioning of a set of observation sequences then automatically identifies where to cut off the hierarchical clustering dendrogram. We show that the method is extremely effective, providing 100% accuracy in separating anomalous from typical behaviors on real-world testbed video surveillance data.

Published in:

Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on

Date of Conference:

19-21 May 2010

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