Title :
Detection and tracking of traffic signs using a recursive Bayesian decision framework
Author :
Marinas, Javier ; Salgado, Luis ; Arróspide, Jon ; Nieto, Marcos
Author_Institution :
Grupo de Tratamiento de Imagenes of the Escuela Tec. Super. de Ing. de Telecomun., Univ. Politec. de Madrid, Madrid, Spain
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.
Keywords :
Kalman filters; image recognition; object detection; traffic engineering computing; Kalman filter; adaptive noise tuning; automatic detection; recursive Bayesian decision framework; road traffic signs; traffic sign recognition; traffic signs detection; traffic signs tracking; Image color analysis; Lighting; Noise; Noise measurement; Roads; Shape; Vehicles;
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4577-2198-4
DOI :
10.1109/ITSC.2011.6082905