Keywords

road models, stereo video, snakes, color segmentation

Abstract

This research focuses on extracting road models from stereo video sequences taken from a moving vehicle. The proposed method combines color histogram based segmentation, active contours (snakes) and morphological processing to extract road boundary coordinates for conversion into Matlab or Multigen OpenFlight compatible polygonal representations. Color segmentation uses an initial truth frame to develop a color probability density function (PDF) of the road versus the terrain. Subsequent frames are segmented using a Maximum Apostiori Probability (MAP) criteria and the resulting templates are used to update the PDFs. Color segmentation worked well where there was minimal shadowing and occlusion by other cars. A snake algorithm was used to find the road edges which were converted to 3D coordinates using stereo disparity and vehicle position information. The resulting 3D road models were accurate to within 1 meter.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2006

Semester

Summer

Advisor

Bauer, Christian

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Science

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0001326

URL

http://purl.fcla.edu/fcla/etd/CFE0001326

Language

English

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Download

Share

COinS