Detection and Tracking of Traffic Signs Using a Recursive Bayesian Decision Framework

Marinas Mateos, Javier and Salgado Álvarez de Sotomayor, Luis and Arróspide Laborda, Jon and Nieto Doncel, Marcos (2011). Detection and Tracking of Traffic Signs Using a Recursive Bayesian Decision Framework. In: "2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)", 05/10/2011 - 07/10/2011, Washington, EEUU. ISBN 978-1-4577-2198-4.

Description

Title: Detection and Tracking of Traffic Signs Using a Recursive Bayesian Decision Framework
Author/s:
  • Marinas Mateos, Javier
  • Salgado Álvarez de Sotomayor, Luis
  • Arróspide Laborda, Jon
  • Nieto Doncel, Marcos
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Event Dates: 05/10/2011 - 07/10/2011
Event Location: Washington, EEUU
Title of Book: Proceedings of 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Date: 2011
ISBN: 978-1-4577-2198-4
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

In this paper we propose a new method for the automatic detection and tracking of road traffic signs using an on-board single camera. This method aims to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. The proposed approach exploits a combination of different features, such as color, appearance, and tracking information. This information is introduced into a recursive Bayesian decision framework, in which prior probabilities are dynamically adapted to tracking results. This decision scheme obtains a number of candidate regions in the image, according to their HS (Hue-Saturation). Finally, a Kalman filter with an adaptive noise tuning provides the required time and spatial coherence to the estimates. Results have shown that the proposed method achieves high detection rates in challenging scenarios, including illumination changes, rapid motion and significant perspective distortion

More information

Item ID: 12199
DC Identifier: http://oa.upm.es/12199/
OAI Identifier: oai:oa.upm.es:12199
Deposited by: Memoria Investigacion
Deposited on: 30 Aug 2012 09:30
Last Modified: 21 Apr 2016 11:24
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