Robust Road Marking Detection and Recognition Using Density-Based Grouping and Machine Learning Techniques
Abstract
This paper presents a robust approach for road marking detection and recognition from images captured by an embedded camera mounted on a car. Our method is designed to cope with illumination changes, shadows, and harsh meteorological conditions. Furthermore, the algorithm can effectively group complex multi-symbol shapes into an individual road marking. For this purpose, the proposed technique relies on MSER features to obtain candidate regions which are further merged using density-based clustering. Finally, these regions of interest are recognized using machine learning approaches. Worth noting, the algorithm is versatile since it does not utilize any prior information about lane position or road space. The proposed method compares favorably to other existing works through a large number of experiments on an extensive road marking dataset.
Cite
Text
Bailo et al. "Robust Road Marking Detection and Recognition Using Density-Based Grouping and Machine Learning Techniques." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017. doi:10.1109/WACV.2017.90Markdown
[Bailo et al. "Robust Road Marking Detection and Recognition Using Density-Based Grouping and Machine Learning Techniques." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017.](https://mlanthology.org/wacv/2017/bailo2017wacv-robust/) doi:10.1109/WACV.2017.90BibTeX
@inproceedings{bailo2017wacv-robust,
title = {{Robust Road Marking Detection and Recognition Using Density-Based Grouping and Machine Learning Techniques}},
author = {Bailo, Oleksandr and Lee, Seokju and Rameau, François and Yoon, Jae Shin and Kweon, In-So},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2017},
pages = {760-768},
doi = {10.1109/WACV.2017.90},
url = {https://mlanthology.org/wacv/2017/bailo2017wacv-robust/}
}