A Method of Planar-Template- Based Camera Self-Calibration for Single-View

Authors: Yue Zhao, Chao Li

Abstract:

Camera calibration is an important step in 3D reconstruction. Camera calibration may be classified into two major types: traditional calibration and self-calibration. However, a calibration method in using a checkerboard is intermediate between traditional calibration and self-calibration. A self is proposed based on a square in this paper. Only a square in the planar template, the camera self-calibration can be completed through the single view. The proposed algorithm is that the virtual circle and straight line are established by a square on planar template, and circular points, vanishing points in straight lines and the relation between them are be used, in order to obtain the image of the absolute conic (IAC) and establish the camera intrinsic parameters. To make the calibration template is simpler, as compared with the Zhang Zhengyou-s method. Through real experiments and experiments, the experimental results show that this algorithm is feasible and available, and has a certain precision and robustness.

Keywords: Absolute conic, camera calibration, circle point, vanishing point.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081981

References:

[1] Hartley R., "Estimation of relative camera positions for uncalibrated cameras", In Proceedings of the European Conference on Computer Vision, pp. 579-387, 1992.
[2] Maybank S.J., Faugeras O.D., "A theory of self-calibration of a moving camera", International Journal of Computer Vision, vol. 8, pp. 123-151,Feb. 1992.
[3] Hartley R., "Self-calibration of stationary cameras", International Journal of Computer Vision, vol. 22, pp. 5-23, Jan. 1997.
[4] Dron L., "Dynamic camera self-calibration of from controlled motion sequences", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 501-506, 1993.
[5] Du F., Brady M., "Self-Calibration of the intrinsic parameters of cameras for active vision systems", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 477-482, 1993.
[6] Pollefeys M., Koch R., Gool L.V., "Self-Calibration and metric reconstruction in spite of varying and unknown internal camera parameters", In Proceedings of the International Conference on Computer Vision, Bombay, pp. 90-95, 1998.
[7] Sturm P., "Critical motion sequences for monocular self-calibration and uncalibrated Euclidean reconstruction", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1100-1105(1997).
[8] Zhang Z., "A flexible new technique for camera calibration", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1330-1334, Nov. 2000.
[9] Meng X.Q., Li H., Hu Z.Y., "A new easy camera calibration technique based on circular points", Journal of Software, vol. 13, No. 5 pp. 957-965, May. 2002.
[10] Wu F.C.,Wang G.H.,Hu Z.Y., "A linear approach for determining intrinsic parameters and pose of cameras from rectangles". Journal of software, vol. 14, pp. 703-712, Feb. 2003.
[11] Xinju Li, Haijiang Zhu, Fuchao Wu, "Camera calibration based on coplanar similar geometrical entities". PR&AI. vol. 17, pp. 457-461, Apr. 2004.
[12] Guanghui Wang, Q.M. Jonathan Wu, Wei Zhang, "Kruppa equation based camera calibration from homography induced by remote plane", Pattern Recognition Letters, vol. 29, pp. 2137-2144, Aug. 2008.
[13] Hui Wang, Yue Zhao, "A new planar circle-based approach for camera self-calibration", Journal of Computational Information Systems, vol. 6, pp, 2877-2883, Sep. 2010.
[14] Yue Zhao, Hui Wang, "Conic and circular points based camera linear calibration", Journal of Information & Computational Science, vol. 7, pp. 2478-2485, Dec. 2010.
[15] Yue Zhao, Hui Wang, Xiaofu Wang, "A Conic-Based Approach for Camera Linear Self-calibration", Journal of Information & Computational Science, vol. 7, pp. 1959-1966, Sep. 2010.
[16] Hui Wang, Yue Zhao, Jianping Li, "Innovation experiment based on circular points and Laguerre theorem in computer vision", In Proceedings of 2nd International Conference on Education Technology and Computer, pp. 25-28, 2010.
[17] Harris C.G., Stephens M. J., "A combined corner and edge detector", In Proceedings of Fourth Alvey Vision Conference, pp. 147-151, 2001.
[18] Yunyoung Nam, Junghun Ryu, "Learning spatio-temporal topology of a multi-camera network by tracking multiple people", Proceedings of World Academy of Science, Engineering And Technology. vol. 24, pp. 175-180, Oct. 2007.
[19] Fuchao Wu, Mathematical methods in computer vision, Beijing: Science Press, 2008.
[20] Hartley R., Zisserman A., Multiple view geometry in computer vision, Cambridge: University Press, 2000.