Road detection and tracking from aerial desert imagery

Yucong Lin, Srikanth Saripalli

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


We present a fast, robust road detection and tracking algorithm for aerial images taken from an Unmanned Aerial Vehicle. A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image. A probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road. The proposed algorithm has been extensively tested on desert images obtained using an Unmanned Aerial Vehicle. Our results indicate that we are able to successfully and accurately detect roads in 96% of the images. We experimentally validated our algorithm on over a thousand aerial images obtained using our UAV. These images consist of straight and curved roads in various conditions with significant changes in lighting and intensity. We have also developed a road-tracking algorithm that searches a local rectangular area in successive images. Initial results are presented that shows the efficacy and the robustness of this algorithm. Using this road tracking algorithm we are able to further improve the road detection and achieve a 98% accuracy.

Original languageEnglish (US)
Pages (from-to)345-359
Number of pages15
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Issue number1-4
StatePublished - Jan 1 2012


  • Hough transform
  • Road detection
  • Road tracking
  • Unmanned Aerial Vehicles

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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