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Machine Vision and Applications

, Volume 22, Issue 6, pp 927–945 | Cite as

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

  • Marcos NietoEmail author
  • Jon Arróspide Laborda
  • Luis Salgado
Original Paper

Abstract

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classifier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

Keywords

ADAS Real-time Plane rectification Bayesian segmentation Kalman filtering Multi-domain vehicle tracking 

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Marcos Nieto
    • 1
    Email author
  • Jon Arróspide Laborda
    • 1
  • Luis Salgado
    • 1
  1. 1.Grupo de Tratamiento de ImágenesUniversidad Politécnica de MadridMadridSpain

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