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Proceedings Paper

Background image understanding and adaptive imaging for vehicle tracking
Author(s): Burak Uzkent; Matthew J. Hoffman; Anthony Vodacek; Bin Chen
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Paper Abstract

We describe our effort to create an imaging-based vehicle tracking system that uses the principles of dynamic data driven applications systems to observe, model, and collect new within a dynamic feedback loop. Several unique aspects of the system include tracking of user-defined vehicles, the use of an adaptive sensor that can change modality, and a reliance on background image understanding to improve tracking and minimize error. We describe the system and show results demonstrated within the DIRSIG image simulation model that show improved tracking results for the system.

Paper Details

Date Published: 19 May 2015
PDF: 7 pages
Proc. SPIE 9460, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XII, 94600F (19 May 2015); doi: 10.1117/12.2177494
Show Author Affiliations
Burak Uzkent, Rochester Institute of Technology (United States)
Matthew J. Hoffman, Rochester Institute of Technology (United States)
Anthony Vodacek, Rochester Institute of Technology (United States)
Bin Chen, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9460:
Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XII
Daniel J. Henry; Gregory J. Gosian; Davis A. Lange; Dale Linne von Berg; Thomas J. Walls; Darrell L. Young, Editor(s)

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