Constraints on general motions for camera calibration with one-dimensional objects

This paper focuses on two problems in camera calibration with one-dimensional (1D) objects: (a) to find out the general motion patterns well suited for solving the calibration problem, and (b) to improve the robustness and accuracy of the method. Firstly, a sufficient and necessary condition for the solvability of 1D calibration with general motions is proved. Then the special motion of tossing a 1D object is provided as an example to illustrate the correctness and feasibility of this condition. After that some practical issues on obtaining the solution are inspected. By avoiding singularities, the precision and robustness of the method are improved: the relative mean errors are reduced to less than 5% at the noise level of one pixel which surpasses the state-of-the-art methods of the same category.

[1]  Anders Heyden,et al.  Degenerate cases and closed-form solutions for camera calibration with one-dimensional objects , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[2]  Stephen J. Maybank,et al.  On plane-based camera calibration: A general algorithm, singularities, applications , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[3]  James H. Williams Fundamentals of Applied Dynamics , 1995 .

[4]  Zhengyou Zhang,et al.  Camera calibration with one-dimensional objects , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Reinhard Koch,et al.  Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[6]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[7]  Janne Heikkilä,et al.  Geometric Camera Calibration Using Circular Control Points , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  E. Petsa,et al.  CAMERA CALIBRATION COMBINING IMAGES WITH TWO VANISHING POINTS , 2004 .

[9]  Long Quan,et al.  Camera calibration and relative pose estimation from gravity , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[10]  Zhanyi Hu,et al.  A new easy camera calibration technique based on circular points , 2003, Pattern Recognit..

[11]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Anders Heyden,et al.  Closed-form Solutions and Degenerate Cases for Camera Calibration with One-Dimensional Objects , 2005, ICCV 2005.

[13]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[14]  Quang-Tuan Luong,et al.  Self-Calibration of a Moving Camera from Point Correspondences and Fundamental Matrices , 1997, International Journal of Computer Vision.

[15]  Andrew Zisserman,et al.  Multiple view geometry in computer visiond , 2001 .

[16]  Zhanyi Hu,et al.  Camera calibration with moving one-dimensional objects , 2005, Pattern Recognit..

[17]  Bill Triggs,et al.  Autocalibration from Planar Scenes , 1998, ECCV.

[18]  Olivier D. Faugeras,et al.  A theory of self-calibration of a moving camera , 1992, International Journal of Computer Vision.

[19]  Bill Triggs,et al.  Autocalibration and the absolute quadric , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  T. Clarke,et al.  The Development of Camera Calibration Methods and Models , 1998 .