Skip to main content

Matchmoving Previsualization Based on Artificial Marker Detection

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1261))

Abstract

In this article, we propose a method for inserting a 3D synthetic object into a video of real scene. The originality of the proposed method lies in the combination and the application to visual effects of different algorithms of computer vision and computer graphics. First, the intrinsic parameters and distortion coefficients of the camera are estimated using a planar checkerboard pattern with Zhang’s algorithm. Then, AruCo marker dictionary and the corresponding feature detection algorithm are used to detect the four corners of a single artificial marker added to the scene. A perspective-4-point method is used to estimate the rotation and the translation of the camera with respect to a 3D reference system attached to the marker. The camera perspective model is then used to project the 3D object on the image plan, while respecting perspective variations when the camera is moving. The 3D object is illuminated with diffuse and specular shading models, in order to match the object to the lighting of the scene. Finally, we conducted an experiment to quantitatively and qualitatively evaluate the stability of the method.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. En, S., Lechervy, A., Jurie, F.: Rpnet: an end-to-end network for relative camera pose estimation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)

    Google Scholar 

  2. Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Medina-Carnicer, R.: Generation of fiducial marker dictionaries using mixed integer linear programming. Pattern Recognit. 51, 481–491 (2016)

    Article  Google Scholar 

  3. Gordon, V.S., Clevenger, J.L.: Computer Graphics Programming in OpenGL with C++. Stylus Publishing, LLC (2018)

    Google Scholar 

  4. Harris, C.G., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  5. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2003)

    Google Scholar 

  6. Hold-Geoffroy, Y., Sunkavalli, K., Hadap, S., Gambaretto, E., Lalonde, J.F.: Deep outdoor illumination estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7312–7321 (2017)

    Google Scholar 

  7. Kendall, A., Grimes, M., Cipolla, R.: Posenet: a convolutional network for real-time 6-DOF camera relocalization. In: Proceedings of the IEEE international conference on computer vision, pp. 2938–2946 (2015)

    Google Scholar 

  8. Kotaru, M., Katti, S.: Position tracking for virtual reality using commodity WiFi. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 68–78 (2017)

    Google Scholar 

  9. Lee, J., Hafeez, J., Kim, K., Lee, S., Kwon, S.: A novel real-time match-moving method with hololens. Appl. Sci. 9(14), 2889 (2019)

    Article  Google Scholar 

  10. Melekhov, I., Ylioinas, J., Kannala, J., Rahtu, E.: Relative camera pose estimation using convolutional neural networks. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 675–687. Springer (2017)

    Google Scholar 

  11. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)

    Article  Google Scholar 

  12. Radke, R.J.: Computer Vision for Visual Effects. Cambridge University Press (2013)

    Google Scholar 

  13. Rambach, J.R., Tewari, A., Pagani, A., Stricker, D.: Learning to fuse: a deep learning approach to visual-inertial camera pose estimation. In: International Symposium on Mixed and Augmented Reality (ISMAR), pp. 71–76. IEEE (2016)

    Google Scholar 

  14. Romero-Ramirez, F.J., Muñoz-Salinas, R., Medina-Carnicer, R.: Speeded up detection of squared fiducial markers. Image Vis. Comput. 76, 38–47 (2018)

    Article  Google Scholar 

  15. Wyman, C., Marrs, A.: Introduction to directx raytracing. In: Ray Tracing Gems, pp. 21–47. Springer (2019)

    Google Scholar 

  16. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Houssam Halmaoui or Abdelkrim Haqiq .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Halmaoui, H., Haqiq, A. (2021). Matchmoving Previsualization Based on Artificial Marker Detection. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_7

Download citation

Publish with us

Policies and ethics