Name
Abstract | ||
|---|---|---|
Vision based lane detection is an essential task in both autonomous lane vehicles research and active safety system development. Hitherto, lane detection is, however, still a challenging issue due to the complexity of the real road scenes. In this paper, we consider lane detection as a visual attention problem. With a Bayesian attention framework, we address the issue from three perspectives: first, lane markings are assumed to be salient in the road scenes, which will pop out driven by the low level features combining with a bottom-up attention mechanism, second, the target-related features are designed guided by a top-down attention strategy, third, the location prior of the lane markings is also investigated. The experimental results show that the proposed lane detection approach is efficient and robust in real scenes. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIG.2011.130 | ICIG |
Keywords | Field | DocType |
road vehicles,road safety,target-related feature,bottom-up attention mechanism,proposed lane detection approach,challenging issue,bayes methods,autonomous lane vehicle,lane detection,top-down attention strategy,lane marking,vision based lane detection,real road scenes complexity,visual attention,bayesian attention framework,object detection,real road scene,driver information systems,visual attention problem,autonomous lane vehicles research,active safety system development,driver assistance system,detectors,edge detection,bayesian methods,bayesian method,visualization,databases | Object detection,Computer vision,Visualization,Computer science,Vision based,Lane detection,Visual attention,Artificial intelligence,Active safety systems,Salient,Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-0-7695-4541-7 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Li | 1 | 446 | 78.39 |
| Xiangjing An | 2 | 226 | 12.15 |
| Hangen He | 3 | 307 | 23.86 |