DocumentCode :
173869
Title :
Lane positioning in highways based on road-sign tracking using Kalman filter
Author :
Hyoungrae Kim ; Jaehong Lee ; Hakil Kim ; Daehyuk Park
Author_Institution :
Sch. of Inf. & Commun. Eng., Inha Univ., Incheon, South Korea
fYear :
2014
fDate :
5-8 Oct. 2014
Firstpage :
2379
Lastpage :
2384
Abstract :
This paper proposes a localization of a vehicle on highway in the cross-sectional direction for the purpose of recognizing the driving lane. By tracking road signs over the highway, the relative position between the vehicle and the sign is calculated and the absolute position is obtained based on the a priori known information of the road sign as traffic regulations for installation. The proposed method uses Kalman filter for road sign tracking, analyzes the motion using the pinhole camera model, and classifies the type of the road sign using ORB (Oriented fast and Rotated BRIEF) features. Then, the driving lane is recognized from the relative position of the vehicle with the road sign. The experiments performed on videos acquired from real-world highway driving demonstrate that the proposed method is capable of compensating the limit of GPS positioning.
Keywords :
Kalman filters; image classification; image motion analysis; object recognition; object tracking; traffic engineering computing; video signal processing; GPS positioning; Kalman filter; ORB features; driving lane recognition; highway driving; lane positioning; motion analysis; oriented fast and rotated BRIEF features; pinhole camera model; road sign classification; road-sign tracking; vehicle localization; videos; Cameras; Feature extraction; Kalman filters; Roads; Tracking; Vehicles; Autonomous vehicle; Kalman filter; Localization; Road sign recognition; Trajectory analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on
Conference_Location :
San Diego, CA
Type :
conf
DOI :
10.1109/SMC.2014.6974282
Filename :
6974282
Link To Document :
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