Abnormal event detection based on trajectory clustering by 2-depth greedy search

Fan Jiang*, Ying Wu, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature. Based on the framework proposed in our previous work [1], we have developed in this paper a new strategy for unsupervised trajectory clustering. More specifically, an information-based trajectory dissimilarity measure is proposed, based on the Bayesian information criterion (BIC). In order to minimize BIC, the agglomerative hierarchical clustering is applied using a 2-depth greedy search process. This strategy achieves better clustering results compared to the traditional 1-depth greedy search. The increased computational complexity is addressed with several bounds on the trajectory dissimilarity.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages2129-2132
Number of pages4
DOIs
StatePublished - Sep 16 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Event detection
  • Unsupervised clustering
  • Video surveillance

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Jiang, F., Wu, Y., & Katsaggelos, A. K. (2008). Abnormal event detection based on trajectory clustering by 2-depth greedy search. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 2129-2132). [4518063] https://doi.org/10.1109/ICASSP.2008.4518063