Robust inverse perspective mapping based on vanishing point

Vision-based road signs detection and recognition has been widely used in intelligent robotics and automotive autonomous driving technology. Currently, one-time calibration of inverse perspective mapping (IPM) parameters is employed to eliminate the effect of perspective mapping, but it is not robust to the uphill and downhill road. We propose an automatic inverse perspective mapping method based on vanishing point, which is adaptive to the uphill and downhill road even with slight rotation of the main road direction. The proposed algorithm is composed of the following three steps: detecting the vanishing point, calculating the pitch and yaw angles and adopting inverse perspective mapping to obtain the “bird's eye view” image. Experimental results show that the adaptability of our inverse perspective mapping framework is comparable to existing state-of-the-art methods, which is conducive to the subsequent detection and recognition of road signs.

[1]  Peyman Moghadam,et al.  Road direction detection based on vanishing-point tracking , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  F. Jaureguizar,et al.  Stabilization of Inverse Perspective Mapping Images based on Robust Vanishing Point Estimation , 2007, 2007 IEEE Intelligent Vehicles Symposium.

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

[4]  Edward Jones,et al.  Distance determination for an automobile environment using Inverse Perspective Mapping in OpenCV , 2010 .

[5]  Fawzi Nashashibi,et al.  Robust real-time lane detection based on lane mark segment features and general a priori knowledge , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[6]  Nan Wang,et al.  The detection and recognition of arrow markings recognition based on monocular vision , 2009, 2009 Chinese Control and Decision Conference.

[7]  Mohamed Aly,et al.  Real time detection of lane markers in urban streets , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[8]  Zhongke Shi,et al.  A Novel Traffic Stream Detection Method Based on Inverse Perspective Mapping , 2012 .

[9]  Sharath Pankanti,et al.  A layered approach to robust lane detection at night , 2009, 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems.

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

[11]  J. Deng,et al.  Fast Lane Detection Based on the B-Spline Fitting , 2013 .

[12]  W. Sardha Wijesoma,et al.  Fast Vanishing-Point Detection in Unstructured Environments , 2012, IEEE Transactions on Image Processing.

[13]  Lai Zhi-min Inverse perspective mapping algorithm suitable for low-resolution , 2013 .

[14]  Ming-Shi Wang,et al.  Topview Transform Model for the vehicle parking assistance system , 2010, 2010 International Computer Symposium (ICS2010).