A Dynamic Hierarchical Clustering Method for Trajectory-Based Unusual Video Event Detection

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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

Transcript of A Dynamic Hierarchical Clustering Method for Trajectory-Based Unusual Video Event Detection

  • Fan Jiang, Ying Wu, Senior Member, IEEE, andAggelos K. Katsaggelos, Fellow, IEEEIEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 18, NO. 4, APRIL 2009

  • IntroductionHidden Markov Model (HMM)

  • Cross Likelihood Ratio (CLR) X = training trajectory Y = likelihood trajectory x, y = 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