Detection of dominant flow and abnormal events in surveillance video

Sooyeong Kwak, Hyeran Byun

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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
Issue number2
Publication statusPublished - 2011 Feb

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (Grant No. 2010-0013737).

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • General Engineering


Dive into the research topics of 'Detection of dominant flow and abnormal events in surveillance video'. Together they form a unique fingerprint.

Cite this