Detection and recognition of urban road markings using images

While road lane markings detection was extensively studied, in particular for intelligent vehicle applications, the detection and recognition of all kind of marking such as arrows, crosswalks, zebras, words, pictograms, continuous and discontinuous lane markings was drastically less studied. However, it has many potential applications in the design of advanced driver assistance systems, as well as for asset management along itineraries. An algorithm is proposed which is based on the following processing steps: marking pixel extraction, detection using connected components before Inverse Perspective Mapping and recognition based on the comparison with a single pattern or with repetitive rectangular patterns. The proposed algorithm is able to detect and recognize repetitive markings (such as crosswalks) as well as single patterns (such as arrows). We believe that the proposed algorithm can be extended easily to solve the problem of the identification of all types of markings.

[1]  Georg Maier,et al.  Real-time detection and classification of arrow markings using curve-based prototype fitting , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[2]  Massimo Bertozzi,et al.  Stereo inverse perspective mapping: theory and applications , 1998, Image Vis. Comput..

[3]  Michael Brady,et al.  Road feature detection and estimation , 2003, Machine Vision and Applications.

[4]  Laurent Smadja,et al.  Global environment interpretation from a new Mobile Mapping System , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[5]  Hiroshi Murase,et al.  Recognition of Road Markings from In-Vehicle Camera Images by a Generative Learning Method , 2009, MVA.

[6]  Dariu Gavrila,et al.  The Issues , 2011 .

[7]  Mohan M. Trivedi,et al.  Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation , 2006, IEEE Transactions on Intelligent Transportation Systems.

[8]  Anton Kummert,et al.  Pedestrian crossing detecting as a part of an urban pedestrian safety system , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[9]  Pierre Charbonnier,et al.  Evaluation of Road Marking Feature Extraction , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[10]  Sergiu Nedevschi,et al.  Detection and classification of painted road objects for intersection assistance applications , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[11]  Mohammad Shorif Uddin,et al.  Robust zebra-crossing detection using bipolarity and projective invariant , 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..

[12]  G. Toulminet,et al.  Image segmentation and pattern recognition for road marking analysis , 2004, 2004 IEEE International Symposium on Industrial Electronics.

[13]  Peifa Jia,et al.  Road Markers Recognition Based on Shape Information , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[14]  G. Thomas,et al.  FREQUENCY FILTERING AND CONNECTED COMPONENTS CHARACTERIZATION FOR ZEBRA-CROSSING AND HATCHED MARKINGS DETECTION , 2010 .

[15]  Shi,et al.  A Fast Algorithm for Finding Crosswalks using Figure-Ground Segmentation , 2006 .

[16]  Pierre Charbonnier,et al.  Robust road marking extraction in urban environments using stereo images , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[17]  J. Little,et al.  Inverse perspective mapping simplifies optical flow computation and obstacle detection , 2004, Biological Cybernetics.