Road Segmentation for Classification of Road Weather Conditions
Abstract
Using vehicle cameras to automatically assess road weather conditions requires that the road surface first be identified and segmented from the imagery. This is a challenging problem for uncalibrated cameras such as removable dash cams or cell phone cameras, where the location of the road in the image may vary considerably from image to image. Here we show that combining a spatial prior with vanishing point and horizon estimators can generate improved road surface segmentation and consequently better road weather classification performance. The resulting system attains an accuracy of 86 % for binary classification (bare vs. snow/ice-covered) and 80 % for 3 classes (dry vs. wet vs. snow/ice-covered) on a challenging dataset.
Cite
Text
Almazan et al. "Road Segmentation for Classification of Road Weather Conditions." European Conference on Computer Vision Workshops, 2016. doi:10.1007/978-3-319-46604-0_7Markdown
[Almazan et al. "Road Segmentation for Classification of Road Weather Conditions." European Conference on Computer Vision Workshops, 2016.](https://mlanthology.org/eccvw/2016/almazan2016eccvw-road/) doi:10.1007/978-3-319-46604-0_7BibTeX
@inproceedings{almazan2016eccvw-road,
title = {{Road Segmentation for Classification of Road Weather Conditions}},
author = {Almazan, Emilio J. and Qian, Yiming and Elder, James H.},
booktitle = {European Conference on Computer Vision Workshops},
year = {2016},
pages = {96-108},
doi = {10.1007/978-3-319-46604-0_7},
url = {https://mlanthology.org/eccvw/2016/almazan2016eccvw-road/}
}