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 language | English |
|---|---|
| Article number | 027202 |
| Journal | Optical Engineering |
| Volume | 50 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2011 Feb 1 |
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All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Engineering(all)
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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 journal › Article
TY - JOUR
T1 - Detection of dominant flow and abnormal events in surveillance video
AU - Kwak, Sooyeong
AU - Byun, Hyeran
PY - 2011/2/1
Y1 - 2011/2/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=81755178982&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81755178982&partnerID=8YFLogxK
U2 - 10.1117/1.3542038
DO - 10.1117/1.3542038
M3 - Article
VL - 50
JO - Optical Engineering
JF - Optical Engineering
SN - 0091-3286
IS - 2
M1 - 027202
ER -