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| Content Provider | Springer Nature Link |
|---|---|
| Author | Zhu, Songhao Hu, Juanjuan Shi, Zhe |
| Copyright Year | 2015 |
| Abstract | To improve the accuracy of the detection of local abnormal behavior, a novel method is here proposed. The main idea of the proposed method is described as follows: firstly, a video sequence is divided into spatio-temporal blobs; then, a statistical method based on the semi-parametric model is adopted to detect these blobs where abnormal behaviors most likely to appear; finally, maximum optical flow energy and local nearest descriptor are utilized to determinate whether these suspicious blobs really contain abnormal behaviors. The experimental results conducted on several benchmarks ademonstrate the effectiveness of the proposed method. |
| Starting Page | 9445 |
| Ending Page | 9459 |
| Page Count | 15 |
| File Format | |
| ISSN | 13807501 |
| Journal | Multimedia Tools and Applications |
| Volume Number | 75 |
| Issue Number | 15 |
| e-ISSN | 15737721 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2015-12-16 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Abnormal behavior Semi-parametric model Optical flow energy Local nearest descriptor Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Hardware and Architecture Software Media Technology |
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