Name
Title | ||
|---|---|---|
Integrating Hyperspectral Likelihoods in a Multidimensional Assignment Algorithm for Aerial Vehicle Tracking. |
Abstract | ||
|---|---|---|
Tracking vehicles through dense environments is an important and challenging task that is mostly tackled using visible and near IR wavelengths. Hyperspectral imaging is known to improve the robustness of target identification, but the massive increase in data created is usually prohibitive for tracking many targets. We present a persistent real-time aerial target tracking system, taking advantage ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/JSTARS.2016.2560220 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Hyperspectral imaging,Target tracking,Radar tracking,Vehicles,Kinematics | Computer vision,Aerial video,Radar tracker,Motion detection,Panchromatic film,Remote sensing,Tracking system,Hyperspectral imaging,Artificial intelligence,Pixel,Vehicle tracking system,Mathematics | Journal |
Volume | Issue | ISSN |
9 | 9 | 1939-1404 |
Citations | PageRank | References |
4 | 0.51 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Burak Uzkent | 1 | 29 | 5.92 |
| Matthew J. Hoffman | 2 | 31 | 5.50 |
| Anthony Vodacek | 3 | 119 | 17.07 |