Title :
Alerting the drivers about road signs with poor visual saliency
Author :
Simon, Ludovic ; Tarel, Jean-Philippe ; Brémond, Roland
Author_Institution :
Lab. for Road Oper., Perception, Simulation & Simulators, Univ. Paris Est, Paris, France
Abstract :
This paper proposes an improvement of advanced driver assistance system based on saliency estimation of road signs. After a road sign detection stage, its saliency is estimated using a SVM learning. A model of visual saliency linking the size of an object and a size-independent saliency is proposed. An eye tracking experiment in context close to driving proves that this computational evaluation of the saliency fits well with human perception, and demonstrates the applicability of the proposed estimator for improved ADAS.
Keywords :
computer vision; driver information systems; learning (artificial intelligence); object detection; road safety; support vector machines; SVM learning; driver alert system; driver assistance system; human perception; object size; road safety; road sign image; visual saliency; Displays; Humans; Image processing; Joining processes; Laboratories; Machine learning; Magnetic heads; Object detection; Road safety; Support vector machines; Advanced Driver Assistance Systems; Conspicuity; Eye-Tracking; Head Up Display; Human Vision; Image Processing; Machine Learning; Object Detection; Road Safety; Road Signs; SVM; Visual Saliency;
Conference_Titel :
Intelligent Vehicles Symposium, 2009 IEEE
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-3503-6
Electronic_ISBN :
1931-0587
DOI :
10.1109/IVS.2009.5164251
