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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yinghuan Shi Yang Gao Ruili Wang |
| Copyright Year | 2010 |
| Abstract | In this paper, we proposed a novel real-time abnormal event detection framework that requires a short training period and has a fast processing speed. Our approach is based on phase correlation and our newly developed spatial-temporal co-occurrence Gaussian mixture models (STCOG)with the following steps: (i) a frame is divided into non-overlapping local regions; (ii) phase correlation is used to estimate the motion vectors between successive two frames for all corresponding local regions, and (iii) STCOG is used to model normal events and detect abnormal events if any deviation from the trained STCOG is found. Our proposed approach is also able to update the parameters incrementally and can be applied in complicated scenes. The proposed approach outperforms previous ones in terms of shorter training periods and lower computational complexity. |
| Starting Page | 3653 |
| Ending Page | 3656 |
| File Size | 1395080 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424475421 |
| ISSN | 10514651 |
| e-ISBN | 9781424475414 |
| DOI | 10.1109/ICPR.2010.891 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Correlation Real time systems Event detection Computational efficiency Training Hidden Markov models Analytical models real-time phase correlation STCOG abnormal event detection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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