| Records |
| Author |
Jose Manuel Alvarez; Antonio Lopez |
| Title |
Model-based road detection using shadowless features and on-line learning |
Type |
Miscellaneous |
| Year |
2009 |
Publication  |
BMVA one–day technical meeting on vision for automotive applications |
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| Keywords |
road detection |
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London, UK |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ AlA2009 |
Serial |
1272 |
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| Author |
Jose Manuel Alvarez; Antonio Lopez |
| Title |
Photometric Invariance by Machine Learning |
Type |
Book Chapter |
| Year |
2012 |
Publication |
Color in Computer Vision: Fundamentals and Applications |
Abbreviated Journal |
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| Volume |
7 |
Issue |
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Pages |
113-134 |
| Keywords |
road detection |
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| Publisher |
iConcept Press Ltd |
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Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek |
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978-0-470-89084-4 |
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ADAS |
Approved |
no |
| Call Number |
Admin @ si @ AlL2012 |
Serial |
2186 |
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| Author |
Jose Manuel Alvarez; Antonio Lopez |
| Title |
Road Detection Based on Illuminant Invariance |
Type |
Journal Article |
| Year |
2011 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
12 |
Issue |
1 |
Pages |
184-193 |
| Keywords |
road detection |
| Abstract |
By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms. |
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no |
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ADAS @ adas @ AlL2011 |
Serial |
1456 |
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| Author |
Jose Manuel Alvarez; Antonio Lopez |
| Title |
Novel Index for Objective Evaluation of Road Detection Algorithms |
Type |
Conference Article |
| Year |
2008 |
Publication |
Intelligent Transportation Systems. 11th International IEEE Conference on, |
Abbreviated Journal |
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Pages |
815–820 |
| Keywords |
road detection |
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Beijing (Xina) |
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ITSC |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ AlL2008 |
Serial |
1074 |
| Permanent link to this record |
