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Evaluation Testbed for ATD/T Performance Prediction (ETAPP)

Award Information
Agency: Department of Defense
Branch: Army
Contract: W31P4Q-05-C-R101
Agency Tracking Number: A043-166-1338
Amount: $119,150.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A04-166
Solicitation Number: 2004.3
Timeline
Solicitation Year: 2004
Award Year: 2005
Award Start Date (Proposal Award Date): 2005-01-12
Award End Date (Contract End Date): 2005-12-10
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
 Scott Ralph
 Senior Scientist
 (617) 491-3474
 sralph@cra.com
Business Contact
 Paul Gonsalves
Title: Vice President
Phone: (617) 491-3474
Email: pgonsalves@cra.com
Research Institution
N/A
Abstract

Currently, common techniques for evaluating automatic target detection/tracking (ATD/T) performance include use simple models, such as quick-look models, or detailed exhaustive simulation. Simple models cannot accurately quantify the performance of candidate algorithms, while the detailed simulation is time consuming, requiring evaluation over each operating condition. A need exists for ATD/T performance prediction based on more accurate models. We propose a method that predicts performance based on image measures quantifying the intrinsic difficulty of ATD/T on the image, as well as using relevant metadata. The propose measures are based on the National Imagery Interpretability Rating Scales (NIIRS) Measure, the Constant False Alarm Rate Measure (CFAR), and the Free Response Operating Characteristic (FROC). We propose a two-phase approach: a learning phase, where the image measures are computed over a set of test images, and the performance of each algorithm is computed; and a performance prediction phase. From the learning phase a model of predicted performance is learned that can be applied across several algorithms and over the various operating conditions, as represented by the test image suite. This model can be used for subsequent rapid evaluation of future algorithms. The testbed has plug-in capability to allow rapid evaluation of new ATD/T algorithms.

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

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