Alerting the drivers about road signs with poor visual saliency

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.

[1]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[2]  Lutz Priese,et al.  New results on traffic sign recognition , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[3]  Sei-Wang Chen,et al.  An automatic road sign recognition system based on a computational model of human recognition processing , 2004, Comput. Vis. Image Underst..

[4]  Visvanathan Ramesh,et al.  A system for traffic sign detection, tracking, and recognition using color, shape, and motion information , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[5]  Arturo de la Escalera,et al.  Traffic sign recognition and analysis for intelligent vehicles , 2003, Image Vis. Comput..

[6]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[7]  Veronica Sundstedt,et al.  Visual attention for efficient high-fidelity graphics , 2005, SCCG '05.

[8]  Jean-Philippe Tarel,et al.  A NEW PARADIGM FOR THE COMPUTATION OF CONSPICUITY OF TRAFFIC SIGNS IN ROAD IMAGES , 2007 .

[9]  B. Cole,et al.  The effect of variability of background elements on the conspicuity of objects , 1984, Vision Research.

[10]  E. Knudsen.  Fundamental components of attention. , 2007, Annual review of neuroscience.

[11]  M. Campani,et al.  Robust road sign detection and recognition from image sequences , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[12]  Lutz Priese,et al.  Ideogram identification in a realtime traffic sign recognition system , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[13]  L. Itti,et al.  Modeling the influence of task on attention , 2005, Vision Research.

[14]  C. Beleznai,et al.  Road Sign Detection from Edge Orientation Histograms , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[15]  W. Ritter,et al.  Hybrid Approach For Traffic Sign Recognition , 1993, Proceedings of the Intelligent Vehicles '93 Symposium.

[16]  John K. Tsotsos,et al.  Neurobiology of Attention , 2005 .

[17]  W. Ritter,et al.  A real-time traffic sign recognition system , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[18]  Jeremy M. Wolfe,et al.  Guided Search 4.0: Current Progress With a Model of Visual Search , 2007, Integrated Models of Cognitive Systems.

[19]  Patrick Le Callet,et al.  A coherent computational approach to model bottom-up visual attention , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  T. Foulsham,et al.  Eye movements during scene inspection: A test of the saliency map hypothesis , 2006 .

[21]  R. Bremond,et al.  La saillance visuelle des objets routiers, un indicateur de la visibilite routiere , 2006 .

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  Nuno Vasconcelos,et al.  Discriminant Saliency for Visual Recognition from Cluttered Scenes , 2004, NIPS.

[24]  P. Parodi,et al.  A feature-based recognition scheme for traffic scenes , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[25]  Jean-Philippe Tarel,et al.  Towards the Estimation of Conspicuity with Visual Priors , 2008, VISAPP.

[26]  B L Cole,et al.  What attracts attention when driving? , 1986, Ergonomics.