Abstract
A motion pattern analysis method for abnormal movement detection is introduced. It analyses motion patterns and detects abnormal movement using partial trajectories, which can be obtained in crowded scenes and are more effective than local motion. In addition, the proposed method is able to deal with noisy data, which is a major cause of false alarms. The experimental results from real-world traffic scene datasets show that the proposed method improves on previous, local motion-based methods.
| Original language | English |
|---|---|
| Pages (from-to) | 186-187 |
| Number of pages | 2 |
| Journal | Electronics Letters |
| Volume | 49 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2013 Jan 31 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
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Motion pattern analysis using partial trajectories for abnormal movement detection in crowded scenes. / Bae, G. T.; Kwak, S. Y.; Byun, H. R.
In: Electronics Letters, Vol. 49, No. 3, 31.01.2013, p. 186-187.Research output: Contribution to journal › Article
TY - JOUR
T1 - Motion pattern analysis using partial trajectories for abnormal movement detection in crowded scenes
AU - Bae, G. T.
AU - Kwak, S. Y.
AU - Byun, H. R.
PY - 2013/1/31
Y1 - 2013/1/31
N2 - A motion pattern analysis method for abnormal movement detection is introduced. It analyses motion patterns and detects abnormal movement using partial trajectories, which can be obtained in crowded scenes and are more effective than local motion. In addition, the proposed method is able to deal with noisy data, which is a major cause of false alarms. The experimental results from real-world traffic scene datasets show that the proposed method improves on previous, local motion-based methods.
AB - A motion pattern analysis method for abnormal movement detection is introduced. It analyses motion patterns and detects abnormal movement using partial trajectories, which can be obtained in crowded scenes and are more effective than local motion. In addition, the proposed method is able to deal with noisy data, which is a major cause of false alarms. The experimental results from real-world traffic scene datasets show that the proposed method improves on previous, local motion-based methods.
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UR - http://www.scopus.com/inward/citedby.url?scp=84877730464&partnerID=8YFLogxK
U2 - 10.1049/el.2012.3340
DO - 10.1049/el.2012.3340
M3 - Article
VL - 49
SP - 186
EP - 187
JO - Electronics Letters
JF - Electronics Letters
SN - 0013-5194
IS - 3
ER -