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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiman Kim Bongnam Kang |
| Copyright Year | 2014 |
| Description | Author affiliation: Pohang Univ. of Sci. & Technol., Pohang, South Korea (Jiman Kim; Bongnam Kang) |
| Abstract | Abandoned object and removed object are important abnormal objects in visual surveillance area to predict the crimes such as explosion or theft event. In real situations, most of existing methods using CCD camera show inconsistent performance because they use a lot of threshold values depending on the environmental conditions of target scene such as illumination change, high traffic volume and complex background. We propose a nonparametric state machine with hierarchical structure consisting of three layers. As shown in the experimental results, the proposed method can be applied to general situations because the state transitions is performed by trained SVM classifiers. |
| Starting Page | 199 |
| Ending Page | 203 |
| File Size | 2028208 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479948710 |
| DOI | 10.1109/AVSS.2014.6918668 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-26 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Shape Image color analysis Databases Conferences Feature extraction Robustness |
| Content Type | Text |
| Resource Type | Article |
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