A dynamic hierarchical clustering method for trajectory-based unusual video event detection

Fan Jiang; Ying Wu; Aggelos K. Katsaggelos

doi:10.1109/TIP.2008.2012070

A dynamic hierarchical clustering method for trajectory-based unusual video event detection

Fan Jiang^*, Ying Wu, Aggelos K. Katsaggelos

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

107 Scopus citations

Abstract

The proposed unusual video event detection method is based on unsupervised clustering of object trajectories, which are modeled by hidden Markov models (HMM). The novelty of the method includes a dynamic hierarchical process incorporated in the trajectory clustering algorithm to prevent model overfitting and a 2-depth greedy search strategy for efficient clustering.

Original language	English (US)
Pages (from-to)	907-913
Number of pages	7
Journal	IEEE Transactions on Image Processing
Volume	18
Issue number	4
DOIs	https://doi.org/10.1109/TIP.2008.2012070
State	Published - 2009

Keywords

Event detection
Unsupervised clustering
Video surveillance

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

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10.1109/TIP.2008.2012070

Cite this

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title = "A dynamic hierarchical clustering method for trajectory-based unusual video event detection",

abstract = "The proposed unusual video event detection method is based on unsupervised clustering of object trajectories, which are modeled by hidden Markov models (HMM). The novelty of the method includes a dynamic hierarchical process incorporated in the trajectory clustering algorithm to prevent model overfitting and a 2-depth greedy search strategy for efficient clustering.",

keywords = "Event detection, Unsupervised clustering, Video surveillance",

author = "Fan Jiang and Ying Wu and Katsaggelos, {Aggelos K.}",

year = "2009",

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AU - Wu, Ying

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AB - The proposed unusual video event detection method is based on unsupervised clustering of object trajectories, which are modeled by hidden Markov models (HMM). The novelty of the method includes a dynamic hierarchical process incorporated in the trajectory clustering algorithm to prevent model overfitting and a 2-depth greedy search strategy for efficient clustering.

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KW - Unsupervised clustering

KW - Video surveillance

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A dynamic hierarchical clustering method for trajectory-based unusual video event detection

Abstract

Keywords

ASJC Scopus subject areas

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