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10 May 2012 Optimized feature-detection for on-board vision-based surveillance
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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.
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Laetitia Gond, David Monnin, and Armin Schneider "Optimized feature-detection for on-board vision-based surveillance", Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 83571N (10 May 2012); https://doi.org/10.1117/12.919730
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