A statistical model for recreational trails in aerial images

Andrew Predoehl, Scott Morris, Kobus Barnard

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


We present a statistical model of aerial images of recreational trails, and a method to infer trail routes in such images. We learn a set of textons describing the images, and use them to divide the image into super-pixels represented by their text on. We then learn, for each text on, the frequency of generating on-trail and off-trail pixels, and the direction of trail through on-trail pixels. From these, we derive an image likelihood function. We combine that with a prior model of trail length and smoothness, yielding a posterior distribution for trails, given an image. We search for good values of this posterior using a novel stochastic variation of Dijkstra's algorithm. Our experiments, on trail images and ground truth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding method.

Original languageEnglish (US)
Article number6618894
Pages (from-to)337-344
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013


  • GIS
  • shortest path
  • statistical model
  • superpixels

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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