Color-Based Road Detection and Its Evaluation on the KITTI Road Benchmark
| Autor: | Bihao Wang, S. A. Rodríguez, Vincent Fremont |
|---|---|
| Přispěvatelé: | Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), Institut d'électronique fondamentale (IEF), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Feature extraction Improved algorithm Binary number [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Standard illuminant 02 engineering and technology computer.software_genre Key issues Image (mathematics) 020901 industrial engineering & automation [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing 11. Sustainability 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Computer vision Segmentation Data mining Artificial intelligence business computer [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | IEEE Intelligent Vehicles Symposium (IV 2014) IEEE Intelligent Vehicles Symposium (IV 2014), Jun 2014, Dearborn, United States. pp.31-36 Intelligent Vehicles Symposium |
| Popis: | International audience; Road detection is one of the key issues of scene understanding for Advanced Driving Assistance Systems (ADAS). Recent approaches has addressed this issue through the use of different kinds of sensors, features and algorithms. KITTI-ROAD benchmark has provided an open-access dataset and standard evaluation mean for road area detection. In this paper, we propose an improved road detection algorithm that provides a pixel-level confidence map. The proposed approach is inspired from our former work based on road feature extraction using illuminant intrinsic image and plane extraction from v-disparity map segmentation. In the former research, detection results of road area are represented by binary map. The novelty of this improved algorithm is to introduce likelihood theory to build a confidence map of road detection. Such a strategy copes better with ambiguous environments, compared to a simple binary map. Evaluations and comparisons of both, binary map and confidence map, have been done using the KITTI-ROAD benchmark. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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