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Proceedings Paper

Comparison of human and algorithmic target detection in passive infrared imagery
Author(s): Bruce A. Weber; Meredith Hutchinson
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Paper Abstract

We have designed an experiment that compares the performance of human observers and a scale-insensitive target detection algorithm that uses pixel level information for the detection of ground targets in passive infrared imagery. The test database contains targets near clutter whose detectability ranged from easy to very difficult. Results indicate that human observers detect more "easy-to-detect" targets, and with far fewer false alarms, than the algorithm. For "difficult-to-detect" targets, human and algorithm detection rates are considerably degraded, and algorithm false alarms excessive. Analysis of detections as a function of observer confidence shows that algorithm confidence attribution does not correspond to human attribution, and does not adequately correlate with correct detections. The best target detection score for any human observer was 84%, as compared to 55% for the algorithm for the same false alarm rate. At 81%, the maximum detection score for the algorithm, the same human observer had 6 false alarms per frame as compared to 29 for the algorithm. Detector ROC curves and observer-confidence analysis benchmarks the algorithm and provides insights into algorithm deficiencies and possible paths to improvement.

Paper Details

Date Published: 5 September 2003
PDF: 10 pages
Proc. SPIE 5075, Targets and Backgrounds IX: Characterization and Representation, (5 September 2003); doi: 10.1117/12.484868
Show Author Affiliations
Bruce A. Weber, Army Research Lab. (United States)
Meredith Hutchinson, Univ. of Maryland/College Park (United States)


Published in SPIE Proceedings Vol. 5075:
Targets and Backgrounds IX: Characterization and Representation
Wendell R. Watkins; Dieter Clement; William R. Reynolds, Editor(s)

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