Vehicle detection in wide-area aerial imagery: cross-association of detection schemes with post-processings
AffiliationUniv Arizona, Dept Elect & Comp Engn
MetadataShow full item record
PublisherINDERSCIENCE ENTERPRISES LTD
CitationGao, 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.
RightsCopyright © 2018 Inderscience Enterprises Ltd.
Collection InformationThis 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 firstname.lastname@example.org.
AbstractPost-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.
Note12 month embargo; available online: 18 Nov 2018
VersionFinal accepted manuscript