Detection of dominant flow and abnormal events in surveillance video

Sooyeong Kwak, Hyeran Byun

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

Original languageEnglish
Article number027202
JournalOptical Engineering
Volume50
Issue number2
DOIs
Publication statusPublished - 2011 Feb 1

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surveillance
Unsupervised learning
performance tests
learning

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

Cite this

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Detection of dominant flow and abnormal events in surveillance video. / Kwak, Sooyeong; Byun, Hyeran.

In: Optical Engineering, Vol. 50, No. 2, 027202, 01.02.2011.

Research output: Contribution to journalArticle

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