Name
Abstract | ||
|---|---|---|
In this paper, we tackle the problem of road detection from RGB images. In particular, we follow a data-driven approach to segmenting the road pixels in an image. To this end, we introduce two road detection methods: A top-down approach that builds an image-level road prior based on the traffic pattern observed in an input image, and a bottom-up technique that estimates the probability that an image superpixel belongs to the road surface in a nonparametric manner. Both our algorithms work on the principle of label transfer in the sense that the road prior is directly constructed from the ground-truth segmentations of training images. Our experimental evaluation on four different datasets shows that this approach outperforms existing top-down and bottom-up techniques, and is key to the robustness of road detection algorithms to the dataset bias. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WACV.2014.6835730 | Applications of Computer Vision |
Keywords | Field | DocType |
image colour analysis,image segmentation,object detection,probability,roads,RGB images,bottom-up technique,data-driven road detection method,ground-truth segmentations,image segmentation,image superpixel,probability,road pixels,traffic pattern | Computer vision,Airfield traffic pattern,Data-driven,Pattern recognition,Computer science,Robustness (computer science),Nonparametric statistics,Image segmentation,Road surface,RGB color model,Pixel,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
2472-6737 | 2 | 0.38 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José María Álvarez | 1 | 468 | 48.77 |
| Mathieu Salzmann | 2 | 3 | 1.07 |
| Nick Barnes | 3 | 577 | 68.68 |