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.
CITATION STYLE
Ouivirach, K., & Dailey, M. N. (2010). Clustering human behaviors with dynamic time warping and hidden Markov models for a video surveillance system. In ECTI-CON 2010 - The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (pp. 884–888).
Mendeley helps you to discover research relevant for your work.