Vehicle tyre parameterisation using binary search thresholding and contour fitting

This paper describes a visual tyre location identification technique using a series of localised edge detection operations coupled with dynamically assigned binary search contrast adjustment to parameterise each of the tyres from a lateral viewpoint image of a vehicle undercarriage. The technique is applied to a data bank of 130 images with an 82% success rate in parameterising the near tyres and 80% success rate in parameterising the far tyres for a given image. The parameters derived from this technique can provide the basis for pose estimation algorithms to perform automatic parking and to aid mobile robots in path planning with respect to motor vehicles.

[1]  Dan Roth,et al.  Learning a Sparse Representation for Object Detection , 2002, ECCV.

[2]  Malik Mallem,et al.  Robust camera pose estimation using 2d fiducials tracking for real-time augmented reality systems , 2004, VRCAI '04.

[3]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Qiang Ji,et al.  3D Face pose estimation and tracking from a monocular camera , 2002, Image Vis. Comput..

[5]  Richard J. Prokop,et al.  A survey of moment-based techniques for unoccluded object representation and recognition , 1992, CVGIP Graph. Model. Image Process..

[6]  Milos Stojmenovic.  Real Time Machine Learning Based Car Detection in Images With Fast Training , 2006, Machine Vision and Applications.

[7]  Brian Leung,et al.  Component-based Car Detection in Street Scene Images , 2004 .

[8]  Guang Deng,et al.  Fast Vision-Based Object Recognition Using Combined Integral Map , 2009, ICVS.

[9]  Alfred M. Bruckstein,et al.  New Devices for 3D Pose Estimation: Mantis Eyes, Agam Paintings, Sundials, and Other Space Fiducials , 2004, International Journal of Computer Vision.

[10]  Robert Ross,et al.  Catadioptric vehicle undercarriage imaging with visual path planning , 2011, The 5th International Conference on Automation, Robotics and Applications.

[11]  Dieter Schmalstieg,et al.  ARToolKitPlus for Pose Trackin on Mobile Devices , 2007 .

[12]  Patrick J. Flynn,et al.  A Survey Of Free-Form Object Representation and Recognition Techniques , 2001, Comput. Vis. Image Underst..

[13]  A. Çapar,et al.  License Plate Recognition From Still Images and Video Sequences: A Survey , 2008, IEEE Transactions on Intelligent Transportation Systems.

[14]  Mark Fiala,et al.  ARTag, a fiducial marker system using digital techniques , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Dieter Schmalstieg,et al.  Pose tracking from natural features on mobile phones , 2008, 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality.

[16]  Wolfram Burgard,et al.  Utilizing reflection properties of surfaces to improve mobile robot localization , 2009, 2009 IEEE International Conference on Robotics and Automation.