Road geometry classification by adaptive shape models

• Verfasst von: Alvarez, José M. [VerfasserIn]
• Verfasst von: Gevers, Theo [VerfasserIn]
• Verfasst von: Diego, Ferran [VerfasserIn]
• Verfasst von: Lopez, Antonio M. [VerfasserIn]
• Titel: Road geometry classification by adaptive shape models
• Verf.angabe: José M. Álvarez, Theo Gevers, Member, IEEE, Ferran Diego, and Antonio M. López
• Jahr: 2013
• Jahr des Originals: 2012
• Umfang: 10 S.
• Teil: volume:14
• Teil: year:2013
• Teil: number:1
• Teil: pages:459-468
• Teil: extent:10
• Fussnoten: Date of Publication: 12 November 2012 ; Gesehen am 12.05.2021
• Titel Quelle: Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on intelligent transportation systems
• Ort Quelle: New York, NY : Inst. of Electrical and Electronics Engineers, 2000
• Jahr Quelle: 2013
• Band/Heft Quelle: 14(2013), 1, Seite 459-468
• DOI: doi:10.1109/TITS.2012.2221088
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Adaptation models
• Sach-SW: Geometry
• Sach-SW: GIST
• Sach-SW: Hidden Markov models
• Sach-SW: holistic representation
• Sach-SW: illuminant invariant
• Sach-SW: image classification
• Sach-SW: Lighting
• Sach-SW: road detection
• Sach-SW: Roads
• Sach-SW: scene classifier
• Sach-SW: scene recognition
• Sach-SW: Shape
• Sach-SW: spatial pyramids
• Sach-SW: support vector machine
• Sach-SW: Training
• K10plus-PPN: 1757759956

Abstract

Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.