Vehicle detection in wide-area aerial imagery: cross-association of detection schemes with post-processings
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IJIM2018Vehicle_Paper.pdf
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Final Accepted Manuscript
Author
Gao, XinAffiliation
Univ Arizona, Dept Elect & Comp EngnIssue Date
2018
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INDERSCIENCE ENTERPRISES LTDCitation
Gao, X. (2018). Vehicle detection in wide-area aerial imagery: cross-association of detection schemes with post-processings. International Journal of Image Mining, 3(2), 106-116.Rights
Copyright © 2018 Inderscience Enterprises Ltd.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Post-processing schemes are crucial for object detection algorithms to improve the performance of detection in wide-area aerial imagery. We select appropriate parameters for three algorithms (variational minimax optimisation (Saha and Ray, 2009), feature density estimation (Gleason et al., 2011) and Zheng's scheme by morphological filtering (Zheng et al., 2013)) to achieve the highest average F-score on random sample frames, and then follow the same procedure to implement five post-processing schemes on each algorithm. Two low-resolution aerial videos are used as our datasets to compare automatic detection results with the ground truth objects on each frame. The performance analysis of post-processing schemes on each algorithm are presented under two sets of evaluation metrics.Note
12 month embargo; available online: 18 Nov 2018ISSN
2055-60392055-6047
Version
Final accepted manuscriptAdditional Links
http://www.inderscience.com/link.php?id=10017603ae974a485f413a2113503eed53cd6c53
10.1504/IJIM.2018.10017603