Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Nieto Doncel, Marcos; Arróspide Laborda, Jon y 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.

Descripción

Título: Road environment modeling using robust perspective analysis and recursive Bayesian segmentation
Autor/es:
  • Nieto Doncel, Marcos
  • Arróspide Laborda, Jon
  • Salgado Álvarez de Sotomayor, Luis
Tipo de Documento: Artículo
Título de Revista/Publicación: Machine Vision And Applications
Fecha: Enero 2011
Volumen: 22
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 10751
Identificador DC: http://oa.upm.es/10751/
Identificador OAI: oai:oa.upm.es:10751
Identificador DOI: 10.1007/s00138-010-0287-7
URL Oficial: http://www.springerlink.com/content/34478581q1850559/
Depositado por: Memoria Investigacion
Depositado el: 29 May 2012 10:04
Ultima Modificación: 20 Abr 2016 18:59
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