Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Semi-Auto Camera Calibration Method for 3D Information Generation

3차원 공간정보 생성을 위한 반자동 카메라 교정 방법

  • Kim, Hyungtae (Dept. of Image Engineering. Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University) ;
  • Paik, Joonki (Dept. of Image Engineering. Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
  • 김형태 (중앙대학교 첨단영상대학원) ;
  • 백준기 (중앙대학교 첨단영상대학원)
  • Received : 2014.12.18
  • Accepted : 2015.04.22
  • Published : 2015.05.25
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Abstract

In this paper, we propose the semi-auto camera calibration method including user input. The proposed method estimates the vanishing points using user defined reference lines and defines the constraint for reducing outlier in vanishing points estimation process. The proposed camera calibration method based on both algebraic and geometric method improves a calibration performance for difficult condition, which represents that existing method can't calibrate a image. Experimental results show that the proposed method calibration accuracy higher than existing method.

본 논문에서는 사용자의 입력을 포함한 반자동 카메라 교정 방법을 제안한다. 제안된 방법은 사용자가 정의한 기준선을 소실점 추정을 위한 정보로 사용하는 동시에, 추정 과정에서 발생하는 아웃라이어 제거를 위한 추가 제약 조건으로 사용한다. 제안된 카메라 교정 방법은 대수적, 기하학적 방법을 모두 사용하여 기존 방법으로는 불가능한 조건에서 교정이 가능하도록 성능을 확장하였다. 교정 실험 결과를 통해 제안하는 방법이 기존 자동 교정보다 교정 정확도가 높은 것을 확인하였다.

Keywords

References

  1. Z. Zhang, "A flexible new technique for camera calibration," IEEE Trans. on Pattern Analysis and Machine Intelligence. vol. 22, no. 11, pp. 1330-1334, November 2000. https://doi.org/10.1109/34.888718
  2. Q. Chen, H. Wu, and T. Wada, "Camera calibration with two arbitrary coplanar circles," European Conf. Computer Vision. pp. 521-532, 2004.
  3. R. Orghidan, J. Salvi, M. Gordan, C. Florea, and J. Batlle, "Structured light self-calibration with vanishing points," Machine Vision and Applications. vol. 25, no. 2, pp. 489-500, February 2014. https://doi.org/10.1007/s00138-013-0517-x
  4. S. Alvarez, D.F. Llorca, and M.A. Sotelo, "Hierarchical camera auto-calibration for traffic surveillance systems," Expert Systems with Applications. vol. 41, no. 4, pp. 1532-1542, March 2014. https://doi.org/10.1016/j.eswa.2013.08.050
  5. S. Alvarez, D.F. Llorca, and M.A. Sotelo, "Camera auto-calibration using zooming and zebra-crossing for traffic monitoring applications," IEEE Int. Conf. on Intelligent Transportation Systems. pp. 608-613, October 2013.
  6. R. Feris, B. Siddiquie, Y. Zhai, J. Petterson, L. brown, and S. Pankanti," attribute-based vehicle search in crowded surveillance videos," Int. Conf. Multimedia Retrieval no. 18, 2011.
  7. J. Chu, L. Wang, R. Feng, and G. Zhang, "Linear camera calibration and pose estimation from vanishing points," Chinese Optics Letters. vol. 10, no. B06, pp. 83-87, April 2012.
  8. B. Li, K. Peng, X. Ying, and H. Zha, "Vanishing point detection using cascaded 1D hough transform from single images," Pattern Recognition Letters. vol. 33, no. 1, pp. 1-8, January 2012. https://doi.org/10.1016/j.patrec.2011.09.027
  9. M. Niteo, and L. Salgado, "Non-linear optimization for robust estimation of vanishing points," IEEE Int. Conf. on Image Processing. pp. 1885-1888, September 2010.
  10. A. Almansa, A. Desolneux, and S. Vamech, "Vanishing point detection without any a priori information," IEEE Trans. on Pattern Analysis and Machine Intelligence. vol. 25, no. 4, pp. 502-507, April 2003. https://doi.org/10.1109/TPAMI.2003.1190575
  11. J. M. Coughlan, and A.L. Yuille, "Manhattan world: orientation and outlier detection by bayesian inference," Neural Computation, vol.15, no., 5, pp.1063-1088, May 2003. https://doi.org/10.1162/089976603765202668
  12. I. Junejo, and H. Foroosh, "Robust auto-calibration from pedestrians," IEEE Int. Conf. on Video and Signal Based Surveillance, pp. 92-97, 2006.
  13. R. Hartley, Multiple view geometry in computer vision, 2nded,. Cambridge university press, 2003.
  14. J.M. Coughlan, and A.L. Yuille, "Manhattan world: orientation and outlier detection by bayesian inference," Neural Computation, vol.15, no., 5, pp.1063-1088, 2003. https://doi.org/10.1162/089976603765202668
  15. E. Guillou, D. Meneveaux, E. Maisel, and K. Bouatouch, 'Using vanishing points for camera calibration and coarse 3D reconstruction from a single image,' The Visual Computer, vol. 16, no. 7, pp.396-410, November 2000. https://doi.org/10.1007/PL00013394
  16. E. Tretiak, O. Barinova, P. Kohli, and V. Lempitsky, "Geometric image parsing in man-made environments," Int. Journel of computer vision, vol. 97, no. 3, pp.305-321, May 2012. https://doi.org/10.1007/s11263-011-0488-1
  17. R. Orghidan, J. Salvi, M. Gordan, and B. Orza, "Camera calibration using two or three vanishing points," IEEE Federated Conf. on Computer Science and Information Systems, pp.123-130, September 2012.
  18. R. Cipolla, T. Drummond, and D. Robertson, "Camera calibration from vanishing points in images of architectural scenes," British Machine Vision Conf. vol.99, pp.382-391, September 1999.
  19. H. Jun, J. Park, and M. Go, "Camera calibration for 3D data acquisition," Workshop on Image Processing and Image Understanding, vol. 9, pp. 203-208, January 1997.
  20. S. Kim, K. Kim, and W. Woo, "Multiple camera calibration for panoramic 3D virtual environment," Journal of the IEEK, vol. 41, no. 2, pp. 137-148, March 2004.