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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Orwell, J. Massey, S. Remagnino, P. Greenhill, D. Jones, G.A. |
| Copyright Year | 1999 |
| Description | Author affiliation: Sch. of Comput. Sci. & Electron. Syst., Kingston Univ., UK (Orwell, J.) |
| Abstract | We describe an architecture for implementing scene understanding algorithms in the visual surveillance domain. To achieve a high level description of events observed by multiple cameras, many inter-related event-driven processes must be executed. We use the agent paradigm to provide a framework in which these processes can be managed. Each camera has an associated agent, which detects and tracks moving regions of interest. This is used to construct and update object agents. Each camera is calibrated so that image co-ordinates can be transformed into ground plane locations. By comparing properties, two object agents can infer that they have the same referent, i.e. that two cameras are observing the same entity, and as a consequence merge identities. Each object's trajectory is classified with a type of activity, with reference to a ground plane agent. We demonstrate objects simultaneously tracked by two cameras, which infer this shared observation. The combination of the agent framework, and visual surveillance application provides an excellent environment for development and evaluation of scene understanding algorithms. |
| Starting Page | 1104 |
| Ending Page | 1107 |
| File Size | 71281 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769500404 |
| DOI | 10.1109/ICIAP.1999.797748 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-09-27 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Surveillance Cameras Electrical capacitance tomography Layout Monitoring Computer science Identity-based encryption Power system security Lapping Vehicles |
| Content Type | Text |
| Resource Type | Article |
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