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Proceedings Paper

Optimized feature-detection for on-board vision-based surveillance
Author(s): Laetitia Gond; David Monnin; Armin Schneider
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Paper Abstract

The detection and matching of robust features in images is an important step in many computer vision applications. In this paper, the importance of the keypoint detection algorithms and their inherent parameters in the particular context of an image-based change detection system for IED detection is studied. Through extensive application-oriented experiments, we draw an evaluation and comparison of the most popular feature detectors proposed by the computer vision community. We analyze how to automatically adjust these algorithms to changing imaging conditions and suggest improvements in order to achieve more exibility and robustness in their practical implementation.

Paper Details

Date Published: 10 May 2012
PDF: 12 pages
Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 83571N (10 May 2012); doi: 10.1117/12.919730
Show Author Affiliations
Laetitia Gond, Institut Franco-Allemand de Recherches de Saint-Louis (France)
David Monnin, Institut Franco-Allemand de Recherches de Saint-Louis (France)
Armin Schneider, Institut Franco-Allemand de Recherches de Saint-Louis (France)


Published in SPIE Proceedings Vol. 8357:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII
J. Thomas Broach; John H. Holloway, Editor(s)

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