Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81300
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dc.contributorInterdisciplinary Division of Aeronautical and Aviation Engineering-
dc.creatorFu, CH-
dc.creatorZhang, YQ-
dc.creatorHuang, ZY-
dc.creatorDuan, R-
dc.creatorXie, ZW-
dc.date.accessioned2019-09-20T00:54:58Z-
dc.date.available2019-09-20T00:54:58Z-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10397/81300-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2019 IEEE. Translations and content mining are permitted for academic research only.en_US
dc.rightsPersonal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.en_US
dc.rightsThe following publication C. Fu, Y. Zhang, Z. Huang, R. Duan and Z. Xie, "Part-Based Background-Aware Tracking for UAV With Convolutional Features," in IEEE Access, vol. 7, pp. 79997-80010, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2922703en_US
dc.subjectVisual object trackingen_US
dc.subjectUnmanned aerial vehicle (UAV)en_US
dc.subjectConvolutional neural networken_US
dc.subjectBackground-aware correlation filteren_US
dc.subjectPart-based strategyen_US
dc.subjectGaussian process regressionen_US
dc.titlePart-based background-aware tracking for UAV with convolutional featuresen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage79997-
dc.identifier.epage80010-
dc.identifier.volume7-
dc.identifier.doi10.1109/ACCESS.2019.2922703-
dcterms.abstractIn recent years, visual tracking is a challenging task in UAV applications. The standard correlation filter (CF) has been extensively applied for UAV object tracking. However, the CF-based tracker severely suffers from boundary effects and cannot effectively cope with object occlusion, which results in suboptimal performance. Besides, it is still a tough task to obtain an appearance model precisely with hand-crafted features. In this paper, a novel part-based tracker is proposed for the UAV. With successive cropping operations, the tracking object is separated into several parts. More specially, the background-aware correlation filters with different cropping matrices are applied. To estimate the translation and scale variation of the tracking object, a structured comparison, and a Bayesian inference approach are proposed, which jointly achieve a coarse-to-fine strategy. Moreover, an adaptive mechanism is used to update the local appearance model of each part with a Gaussian process regression method. To construct a better appearance model, features extracted from the convolutional neural network are utilized instead of hand-crafted features. Through extensive experiments, the proposed tracker reaches competitive performance on 123 challenging UAV image sequences and outperforms other 20 popular state-of-the-art visual trackers in terms of overall performance and different challenging attributes.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 2019, v. 7, p. 79997-80010-
dcterms.isPartOfIEEE access-
dcterms.issued2019-
dc.identifier.isiWOS:000474601000001-
dc.identifier.scopus2-s2.0-85068993900-
dc.description.validate201909 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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