Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Nieto Doncel, Marcos and Arróspide Laborda, Jon and Salgado Álvarez de Sotomayor, Luis (2011). Road environment modeling using robust perspective analysis and recursive Bayesian segmentation. "Machine Vision And Applications", v. 22 (n. 6); pp. 927-945. ISSN 0932-8092. https://doi.org/10.1007/s00138-010-0287-7.

Description

Title: Road environment modeling using robust perspective analysis and recursive Bayesian segmentation
Author/s:
  • Nieto Doncel, Marcos
  • Arróspide Laborda, Jon
  • Salgado Álvarez de Sotomayor, Luis
Item Type: Article
Título de Revista/Publicación: Machine Vision And Applications
Date: January 2011
ISSN: 0932-8092
Volume: 22
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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 classi- fier, 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.

More information

Item ID: 10751
DC Identifier: http://oa.upm.es/10751/
OAI Identifier: oai:oa.upm.es:10751
DOI: 10.1007/s00138-010-0287-7
Official URL: http://www.springerlink.com/content/34478581q1850559/
Deposited by: Memoria Investigacion
Deposited on: 29 May 2012 10:04
Last Modified: 20 Apr 2016 18:59
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