A post-processing scheme for the performance improvement of vehicle detection in wide-area aerial imagery
AffiliationUniv Arizona, Dept Elect & Comp Engn
MetadataShow full item record
PublisherSPRINGER LONDON LTD
CitationGao, X, Ram, S., & Rodríguez, J.J. A post-processing scheme for the performance improvement of vehicle detection in wide-area aerial imagery. SIViP (2019). http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s11760-019-01592-4
Rights© Springer-Verlag London Ltd., part of Springer Nature 2019.
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AbstractIn this paper, we present a post-processing scheme to improve the performance of vehicle detection in wide-area aerial imagery. Using low-resolution aerial frames for the performance analysis, we adapted nine algorithms for vehicle detection. We derived a three-stage scheme to measure performance improvement on the selected five object segmentation algorithms before and after post-processing. We compared automatic detections results to ground-truth objects, and classified each type of detections in terms of true positive, false negative and false positive. Several evaluation metrics are adopted for the experimental study.
DescriptionIncluded is an Erratum to the article, published February, 2020.
Note12 month embargo; published online: 8 November 2019
VersionFinal accepted manuscript