Name
Abstract | ||
|---|---|---|
We consider the optical remote sensing tracking problem for vehicles in a complex environment using an adaptive sensor that can take spectral data at a small number of locations. The Dynamic Data-Driven Applications Systems (DDDAS) paradigm is well-suited for dynamically controlling such an adaptive sensor by using the prediction of object movement and its interaction with the environment to guide the location of spectral measurements. The spectral measurements are used for target identification through feature matching. We consider several adaptive sampling strategies for how to assign locations for spectral measurements in order to distinguish between multiple targets. In addition to guiding the measurement process, the tracking system pulls in additional data from OpenStreetMap to identify road networks and intersections. When a vehicle enters a detected intersection, it triggers the use of a multiple model prediction system to sample all possible turning options. The result of this added information is more accurate predictions and analysis from data assimilation using a Gaussian Sum filter (GSF). (C) 2013 The Authors. Published by Elsevier B.V. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.procs.2013.05.363 | 2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE |
Keywords | Field | DocType |
Dynamic Data Driven Application Systems, DDDAS, data assimilation, Target tracking, Feature matching | Small number,Computer vision,Data mining,Road networks,Computer science,Adaptive sampling,Tracking system,Video tracking,Feature matching,Artificial intelligence,Data assimilation,Predictive modelling | Conference |
Volume | ISSN | Citations |
18 | 1877-0509 | 5 |
PageRank | References | Authors |
0.44 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Burak Uzkent | 1 | 29 | 5.92 |
| Matthew J. Hoffman | 2 | 31 | 5.50 |
| Anthony Vodacek | 3 | 119 | 17.07 |
| John P. Kerekes | 4 | 194 | 35.38 |
| Bin Chen | 5 | 5 | 0.44 |