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A Dynamic Hierarchical Clustering Method for Trajectory-Based Unusual Video Event Detection. Fan Jiang, Ying Wu , Senior Member, IEEE, and Aggelos K. Katsaggelos , Fellow, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 18, NO. 4, APRIL 2009. Introduction. Hidden Markov Model (HMM). - PowerPoint PPT Presentation
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Fan Jiang, Ying Wu, Senior Member, IEEE, and
Aggelos K. Katsaggelos, Fellow, IEEEIEEE TRANSACTIONS ON IMAGE
PROCESSING, VOL. 18, NO. 4, APRIL 2009
IntroductionHidden Markov Model (HMM)
Cross Likelihood Ratio (CLR)X = training trajectoryY = likelihood trajectoryλx, λy = HMM of X or Y
Bayesian Information Criterion (BIC)
Dissimilarity
TRAJECTORY CLUSTERINGDynamic hierarchical clustering (DHC)
Fifteen categories of any three trajectory groups according to different nearest neighbors
Merging can be rejected (exclusion) if
Substituting BIC
Where :
Assume :
Sufficient condition to be satisfied
2-depth greedy search algorithm
NORMAL CLUSTER IDENTIFICATION ANDABNORMALITY DETECTION
If
then trajectory i is unusual
Examples of normal (a)–(d) and unusual (e)–(h) trajectories.
Thank You