抄録
Road lane detection is a key problem in advanced driver-assistance systems (ADAS). For solving this problem, vision-based detection methods are widely used and are generally focused on edge information. However, only using edge information leads to miss detection and error detection in various road conditions. In this paper, we propose a neighbor-based image conversion method, called extremal-region enhancement. The proposed method enhances the white lines in intensity, hence it is robust to shadows and illuminance changes. Both edge and shape information of white lines are extracted as lane features in the method. In addition, we implement a robust road lane detection algorithm using the extracted features and improve the correctness through probability tracking. The experimental result shows an average detection rate increase of 13.2% over existing works.
本文言語 | English |
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ホスト出版物のタイトル | Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 |
出版社 | Institute of Electrical and Electronics Engineers Inc. |
ページ | 519-523 |
ページ数 | 5 |
ISBN(電子版) | 9781479961009 |
DOI | |
出版ステータス | Published - 2016 6月 7 |
イベント | 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 - Kuala Lumpur, Malaysia 継続期間: 2016 11月 3 → 2016 11月 6 |
Other
Other | 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 |
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国/地域 | Malaysia |
City | Kuala Lumpur |
Period | 16/11/3 → 16/11/6 |
ASJC Scopus subject areas
- コンピュータ ビジョンおよびパターン認識