Efficient road detection and tracking for unmanned aerial vehicle

Zhou, Hailing, Kong, Hui, Wei, Lei, Creighton, Douglas and Nahavandi, Saeid 2015, Efficient road detection and tracking for unmanned aerial vehicle, IEEE Transactions on intelligent transportation systems, vol. 16, no. 1, pp. 297-309.

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Title Efficient road detection and tracking for unmanned aerial vehicle
Author(s) Zhou, HailingORCID iD for Zhou, Hailing orcid.org/0000-0001-5009-4330
Kong, Hui
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 IEEE Transactions on intelligent transportation systems
Volume number 16
Issue number 1
Start page 297
End page 309
Total pages 13
Publisher Institute of Electrical and Electronics Engineers
Place of publication Champaign, III.
Publication date 2015
ISSN 1524-9050
Keyword(s) GraphCut algorithm
road detection
road tracking
unmanned aerial vehicle (UAV)
Summary Abstract - An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground–vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut–based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 x 595 videos on average.
Language eng
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072549

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