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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rice, A. Vasquez, J. |
| Copyright Year | 2011 |
| Description | Author affiliation: Numerica Corporation Dayton, OH., U.S.A. (Rice, A.; Vasquez, J.) |
| Abstract | A methodology for the context-aided-tracking of ground vehicles in remote airborne imagery is presented in which a background model is inferred from hyperspectral imagery. The functional materials in a scene are determined and become a background model. Here, a manual method of forming the model is presented, as well as a novel autonomous method which exploits the emerging class of adaptive multiple-object-spectrometer instruments. A multiple-hypothesis-tracker is introduced, which relies on background statistics to form track costs and associated track maintenance thresholds. These statistics include detectability, track density, and false measurement density. Traditionally, these statistics are uniform constants, but the advent of the background model allows for spatially-varying statistics. The context-aided-tracker, which uses these statistics, is shown to achieve an increase in performance. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 727972 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781457702679 |
| e-ISBN | 9780982443828 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Intl Society of Info Fusion |
| Subject Keyword | Insulation life Degradation Materials Adaptation models Pixel Atmospheric modeling Indexes adaptive sensing Tracking data association Kalman filtering estimation hypersectral context aiding track maintenance multiple object spectrometer |
| Content Type | Text |
| Resource Type | Article |
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