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. Attached Files Name Description MIMEType Size Downloads Title Orientation aware vehicle detection in aerial images Author(s) Zhou, Hailing orcid.org/0000-0001-5009-4330 Wei, Lei orcid.org/0000-0001-8267-0283 Creighton, Douglas orcid.org/0000-0002-9217-1231 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) Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 12 Jul 2018, 09:55:53 EST Fri, 13 Jul 2018, 05:01:31 EST Fri, 13 Jul 2018, 06:05:04 EST Tue, 27 Nov 2018, 13:50:12 EST Tue, 27 Nov 2018, 13:51:45 EST Thu, 15 Oct 2020, 11:09:21 EST Sun, 07 Nov 2021, 12:45:12 EST Sun, 07 Nov 2021, 13:29:23 EST Sun, 07 Nov 2021, 13:32:18 EST Filtered Full Citation counts:   Cited 9 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 21 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 12 Jul 2018, 09:55:53 EST