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Orientation aware vehicle detection in aerial images

Zhou, Hailing, Wei, Lei, Creighton, Douglas and Nahavandi, Saeid 2017, Orientation aware vehicle detection in aerial images, Electronics letters, vol. 53, no. 21, pp. 1406-1408, doi: 10.1049/el.2017.2087.

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Title Orientation aware vehicle detection in aerial images
Author(s) Zhou, HailingORCID iD for Zhou, Hailing orcid.org/0000-0001-5009-4330
Wei, LeiORCID iD for Wei, Lei orcid.org/0000-0001-8267-0283
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Electronics letters
Volume number 53
Issue number 21
Start page 1406
End page 1408
Total pages 2
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2017-11-02
ISSN 0013-5194
Keyword(s) autonomous aerial vehicles
remote sensing
geophysical image processing
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
Summary Vehicle detection in aerial images is of great interest in the field of remote sensing. Many methods such as the sliding-window-based detection have been successfully developed. A simple and effective mechanism to improve the existing methods is proposed. Vehicle in aerial images usually appears in arbitrary directions. Previous algorithms need to repeat the search at a pixel with all the possible orientations, which often bring the problem of increasing false alarms and computational complexity. To solve this problem, image local orientation is introduced into detection that provides a proper search direction for each pixel. Experimental results on a public database, unmanned aerial vehicle (UAV) and satellite images demonstrate the effectiveness and promising improvements in comparison with existing techniques.
Language eng
DOI 10.1049/el.2017.2087
Indigenous content off
Field of Research 0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
1005 Communications Technologies
HERDC Research category C4.1 Letter or note
Copyright notice ©2017, The Institution of Engineering and Technology
Persistent URL http://hdl.handle.net/10536/DRO/DU:30110974

Document type: Journal Article
Collection: Institute for Intelligent Systems Research and Innovation (IISRI)
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Created: Thu, 12 Jul 2018, 09:55:53 EST

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