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    Vehicle detection in wide-area aerial imagery: cross-association of detection schemes with post-processings

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    Name:
    IJIM2018Vehicle_Paper.pdf
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    407.5Kb
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    Description:
    Final Accepted Manuscript
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    Author
    Gao, Xin
    Affiliation
    Univ Arizona, Dept Elect & Comp Engn
    Issue Date
    2018
    Keywords
    post-processing
    object detection
    wide-area aerial imagery
    
    Metadata
    Show full item record
    Publisher
    INDERSCIENCE ENTERPRISES LTD
    Citation
    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.
    Journal
    International Journal of Image Mining
    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 2018
    ISSN
    2055-6039
    2055-6047
    DOI
    10.1504/IJIM.2018.10017603
    Version
    Final accepted manuscript
    Additional Links
    http://www.inderscience.com/link.php?id=10017603
    ae974a485f413a2113503eed53cd6c53
    10.1504/IJIM.2018.10017603
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