You are here

AUTOMATED VISUAL DATABASE CREATION FOR A GROUND VEHICLE SIMULATOR

Download pdf | Full Screen View

Date Issued:
2006
Abstract/Description:
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.
Title: AUTOMATED VISUAL DATABASE CREATION FOR A GROUND VEHICLE SIMULATOR.
15 views
9 downloads
Name(s): Claudio, Pedro, Author
Bauer, Christian, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2006
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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.
Identifier: CFE0001326 (IID), ucf:46994 (fedora)
Note(s): 2006-08-01
Ph.D.
Engineering and Computer Science, School of Electrical Engineering and Computer Science
Doctorate
This record was generated from author submitted information.
Subject(s): road models
stereo video
snakes
color segmentation
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0001326
Restrictions on Access: public
Host Institution: UCF

In Collections