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

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).

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

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  1. A Dynamic Hierarchical Clustering Method forTrajectory-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

  2. Introduction • Hidden Markov Model (HMM)

  3. Cross Likelihood Ratio (CLR)X = training trajectoryY = likelihood trajectoryλx, λy = HMM of X or Y

  4. Bayesian Information Criterion (BIC) • Dissimilarity

  5. TRAJECTORY CLUSTERING • Dynamic hierarchical clustering (DHC)

  6. Fifteen categories of any three trajectory groups according to different nearest neighbors

  7. Merging can be rejected (exclusion) if • Substituting BIC • Where :

  8. Assume : • Sufficient condition to be satisfied

  9. 2-depth greedy search algorithm

  10. NORMAL CLUSTER IDENTIFICATION ANDABNORMALITY DETECTION • Ifthen trajectory i is unusual

  11. Examples of normal (a)–(d) and unusual (e)–(h) trajectories.

  12. Thank You

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