Methods to Detect Road Features for Video-Based In-Vehicle Navigation Systems
- Authors
- Choi, Kyoung-Ho; Park, Soon-Young; Kim, Seong-Hoon; Lee, Ki-Sung; Park, Jeong-Ho; Cho, Seong-Ik; Park, Jong-Hyun
- Issue Date
- 2010
- Publisher
- TAYLOR & FRANCIS INC
- Keywords
- Bayesian network; driving-lane recognition; lane-color recognition; lane detection; support vector machines; video-based navigation system
- Citation
- JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, v.14, no.1, pp.13 - 26
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
- Volume
- 14
- Number
- 1
- Start Page
- 13
- End Page
- 26
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/118607
- DOI
- 10.1080/15472450903386005
- ISSN
- 1547-2450
- Abstract
- Understanding road features such as position and color of lane markings in a live video captured from a moving vehicle is essential in building video-based car navigation systems. In this article, the authors present a framework to detect road features in 2 difficult situations: (a) ambiguous road surface conditions (i.e., damaged roads and occluded lane markings caused by the presence of other vehicles on the road) and (b) poor illumination conditions (e.g., backlight, during sunset). Furthermore, to understand the lane number that a driver is driving on, the authors present a Bayesian network (BN) model, which is necessary to support more sophisticated navigation services for drivers such as recommending lane change at an appropriate time before turning left or right at the next intersection. In the proposed BN approach, evidence from (1) a computer vision engine (e.g., lane-color detection) and (2) a navigation database (e.g., the total number of lanes) was fused to more accurately decide the lane number. Extensive simulation results indicated that the proposed methods are both robust and effective in detecting road features for a video-based car navigation system.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Bioengineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.