Name
Abstract | ||
|---|---|---|
For surveillance robots, road detection is of high importance for other functionalities such as pedestrian detection, obstacle avoidance, autonomous running, etc. The vision-based road detection is to classify image pixels belonging to road surface or not. Up to now, most algorithms are designed for working during daytime. In this paper, we focus on road detection at night. Firstly a planar reflection model is used to fit the intensity distribution of the images pixels got from a near-infrared camera. After that, we use a pixel-based classification to determine whether the pixel belongs to the road surface or not. In the experiments, we compare our algorithm with the region growing method. The results show that our approach works better in several aspects. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICInfA.2013.6720465 | Information and Automation |
Keywords | Field | DocType |
image pixel classification,road surface,pedestrian detection,pixel-based classification,road detection,region growing method,near-infrared camera,surveillance robots,night working,planar reflection model,vision-based road detection,image classification,obstacle avoidance,object detection,intensity distribution,autonomous running,collision avoidance,robot vision | Obstacle avoidance,Computer vision,Object detection,Object-class detection,Computer science,Road surface,Region growing,Artificial intelligence,Pixel,Contextual image classification,Pedestrian detection | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Tang | 1 | 5 | 1.91 |
| Qunqun Xie | 2 | 5 | 1.23 |
| Guolai Jiang | 3 | 31 | 3.55 |
| Yongsheng Ou | 4 | 243 | 42.32 |
| Yangsheng Xu | 5 | 1541 | 245.29 |