Name
Abstract | ||
|---|---|---|
Vision-based road detection is a key component for autonomous vehicle. Existing techniques could be roughly categorized into two categories: off-line training based algorithms and on-line learning based algorithms. While off-line training based algorithms may not adapt well to the new testing scenario, on-line learning based algorithms may not produce robust results. In this paper, we present a method that combines the merits of both off-line and on-line algorithms. Firstly, we get the likelihood image using road and background detectors based on mixture models. Then, the likelihood image is combined with the result generated by classifier which is trained using off-line booting. And the graph cut segmentation will be performed to get an accurate road region. Experiments on road sequences of unstructured road show that the proposed method provides high road detection accuracy when compared to state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIT.2014.36 | CIT |
Keywords | DocType | ISSN |
expectation-maximization,road vehicles,off-line learning,learning (artificial intelligence),image segmentation,expectation-maximization, adaboost, graph-cut,off-line booting,adaboost,mixture models,vision-based road detection,image classification,autonomous vehicle,image sequences,image classifier,object detection,off-line training based algorithms,computer vision,likelihood image,graph theory,graph cut segmentation,unstructured road,graph-cut,road sequences,on-line learning,online learning based algorithms,road detectors,background detectors | Conference | 2474-9648 |
Citations | PageRank | References |
1 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Xie | 1 | 1 | 0.34 |
| Meiping Shi | 2 | 1 | 0.68 |
| Hao Fu | 3 | 10 | 2.51 |
| Tao Wu | 4 | 58 | 11.53 |