Fast road detection and tracking in aerial videos

Zhou,H, Kong,H, Alvarez,J, Creighton,D and Nahavandi,S 2014, Fast road detection and tracking in aerial videos, in Proceedings of 25th Intelligent Vehicles Symposium; IEEE, 2014, IEEE, Piscataway, NJ, pp. 712-718, doi: 10.1109/IVS.2014.6856523.

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Title Fast road detection and tracking in aerial videos
Author(s) Zhou,HORCID iD for Zhou,H orcid.org/0000-0001-5009-4330
Kong,H
Alvarez,J
Creighton,DORCID iD for Creighton,D orcid.org/0000-0002-9217-1231
Nahavandi,SORCID iD for Nahavandi,S orcid.org/0000-0002-0360-5270
Conference name Intelligent Vehicles. Symposium (25th: 2014: Dearborn, Michigan)
Conference location Dearborn, MI
Conference dates 2014/6/8 - 2014/6/11
Title of proceedings Proceedings of 25th Intelligent Vehicles Symposium; IEEE, 2014
Editor(s) [Unknown]
Publication date 2014
Conference series Intelligent Vehicles Symposium
Start page 712
End page 718
Total pages 6
Publisher IEEE
Place of publication Piscataway, NJ
Summary  We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average.
Language eng
DOI 10.1109/IVS.2014.6856523
Field of Research 080103 Computer Graphics
080106 Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072696

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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