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Real-time Adaptive Classification Environment using Rules (RACER)

Award Information
Agency: Department of Defense
Branch: Army
Contract: DAAD19-02-C-0092
Agency Tracking Number: 44156-CI
Amount: $100,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 2002
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Mark Stevens
 Senior Scientist
 (617) 491-3474
 mstevens@cra.com
Business Contact
 Paul Gonsalves
Title: Vice President
Phone: (617) 491-3474
Email: pgonsalves@cra.com
Research Institution
 Boston University
 Allen M Waxman
 
Cognitive and Neural Systems, 677 Beacon Street
Boston, MA 02215
United States

 (617) 353-6743
 Nonprofit College or University
Abstract

"Robust Automatic Target Recognition (ATR) algorithms must be able to identify the conditions under which they are operating and tune their parameters to ensure targets are identified while retaining a low false-alarm rate. For example, the image signatureof a distant target is fundamentally different from the signature of a near target, so a single ATR algorithm with a single set of parameters will not perform optimally for both conditions. We propose an adaptive classification system called RACER(Real-time Adaptive Classification Environment using Rules). RACER uses meta-features, provided by a human operator or inferred from the scene, to identify the current operat-ing condition. Once the operating condition is identified, the on-line ATR'sclassifier and feature extraction parame-ters are tuned to maximize target recognition accuracy. The on-line tuning will be rule-based, with rules either speci-fied by an operator or automatically through reinforcement learning. A method for combining theoutput of multiple classifiers using Dempster-Shafer evidence combination will also be explored. Finally, a requirements study on hardware for a Phase II real-time implementation will be conducted. This includes assessing existing COTS recon-figurable FPGAboards, as well as compilers from C/C++ to the target FPGA. We see several potential applications of the proposed technology: 1) direct application of RACER to DoD ATR pro-grams, and 2) generalization of the classifier and learning algorithms to otherdomains, in particular the computer vision industry such as target learning & tracking of people using multi/hyperspectral imagery with applications to Homeland security."

* Information listed above is at the time of submission. *

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