Road geometry classification by adaptive shape models

José M. Alvarez, Theo Gevers, Ferran Diego, Antonio M. Lopez

Research output: Contribution to journalArticleResearchpeer-review

21 Citations (Scopus)


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. © 2000-2011 IEEE.
Original languageEnglish
Article number6342913
Pages (from-to)459-468
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number1
Publication statusPublished - 1 Jan 2013


  • GIST
  • holistic representation
  • illuminant invariant
  • image classification
  • road detection
  • scene classifier
  • scene recognition
  • spatial pyramids
  • support vector machine


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