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| Content Provider | Springer Nature Link |
|---|---|
| Author | Tian, Ying li Brown, Lisa Hampapur, Arun Lu, Max Senior, Andrew Shu, Chiao fe |
| Copyright Year | 2008 |
| Abstract | The increasing need for sophisticated surveillance systems and the move to a digital infrastructure has transformed surveillance into a large scale data analysis and management challenge. Smart surveillance systems use automatic image understanding techniques to extract information from the surveillance data. While the majority of the research and commercial systems have focused on the information extraction aspect of the challenge, very few systems have explored the use of extracted information in the search, retrieval, data management and investigation context. The IBM smart surveillance system (S3) is one of the few advanced surveillance systems which provides not only the capability to automatically monitor a scene but also the capability to manage the surveillance data, perform event based retrieval, receive real time event alerts thru standard web infrastructure and extract long term statistical patterns of activity. The IBM S3 is easily customized to fit the requirements of different applications by using an open-standards based architecture for surveillance. |
| Starting Page | 315 |
| Ending Page | 327 |
| Page Count | 13 |
| File Format | |
| ISSN | 09328092 |
| Journal | Machine Vision and Applications |
| Volume Number | 19 |
| Issue Number | 5 |
| e-ISSN | 14321769 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2008-06-24 |
| Publisher Place | Berlin/Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Communications Engineering, Networks Image Processing and Computer Vision Pattern Recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture |
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