Name
Abstract | ||
|---|---|---|
The ability to automatically determine the road type from sensor data is of great significance for automatic annotation of routes and autonomous navigation of robots and vehicles. In this paper, we present a novel algorithm for content-based road type classification from images. The proposed method learns discriminative features from training data in an unsupervised manner, thus not requiring domain-specific feature engineering. This is an advantage over related road surface classification algorithms which are only able to make a distinction between pre-specified uniform terrains. In order to evaluate the proposed approach, we have constructed a challenging road image dataset of 20,000 samples from real-world road images in the paved and unpaved road classes. Experimental results on this dataset show that the proposed algorithm can achieve state-of-the-art performance in road type classification. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.409 | ICPR |
Keywords | DocType | ISSN |
image classification,learning (artificial intelligence),automatic annotation,autonomous navigation,content-based road type classification,discriminative features,image-based road type classification,real-world road images,training data,unsupervised manner | Conference | 1051-4651 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viktor Slavkovikj | 1 | 79 | 5.41 |
| Steven Verstockt | 2 | 55 | 13.58 |
| Wesley De Neve | 3 | 525 | 54.41 |
| Sofie Van Hoecke | 4 | 113 | 26.27 |
| Rik Van de Walle | 5 | 2040 | 238.28 |
| De Neve, W. | 6 | 11 | 1.96 |
| Van Hoecke, S. | 7 | 0 | 0.34 |