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Proceedings Paper

AKITA: Application Knowledge Interface to Algorithms
Author(s): Paul Barros; Allison Mathis; Kevin Newman; Steven Wilder
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Paper Abstract

We propose a methodology for using sensor metadata and targeted preprocessing to optimize which selection from a large suite of algorithms are most appropriate for a given data set. Rather than applying several general purpose algorithms or requiring a human operator to oversee the analysis of the data, our method allows the most effective algorithm to be automatically chosen, conserving both computational, network and human resources. For example, the amount of video data being produced daily is far greater than can ever be analyzed. Computer vision algorithms can help sift for the relevant data, but not every algorithm is suited to every data type nor is it efficient to run them all. A full body detector won’t work well when the camera is zoomed in or when it is raining and all the people are occluded by foul weather gear. However, leveraging metadata knowledge of the camera settings and the conditions under which the data was collected (generated by automatic preprocessing), face or umbrella detectors could be applied instead, increasing the likelihood of a correct reading. The Lockheed Martin AKITA™ system is a modular knowledge layer which uses knowledge of the system and environment to determine how to most efficiently and usefully process whatever data it is given.

Paper Details

Date Published: 3 June 2013
PDF: 12 pages
Proc. SPIE 8744, Automatic Target Recognition XXIII, 87440T (3 June 2013); doi: 10.1117/12.2018630
Show Author Affiliations
Paul Barros, Lockheed Martin Corp. (United States)
Allison Mathis, Lockheed Martin Corp. (United States)
Kevin Newman, Lockheed Martin Corp. (United States)
Steven Wilder, Lockheed Martin Corp. (United States)

Published in SPIE Proceedings Vol. 8744:
Automatic Target Recognition XXIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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