AUTOMATIC AUGMENTED VIDEO CREATION FOR MARKERLESS ENVIRONMENTS

J. Sánchez, D. Borro

2007

Abstract

In this paper we present an algorithm to calculate the camera motion in a video sequence. Our method can search and track feature points along the video sequence, calibrate pinhole cameras and estimate the camera motion. In the first step, a 2D feature tracker finds and tracks points in the video. Using this information, outliers are detected using epipolar geometry robust estimation techniques. Finally, the geometry is refined using non linear optimization methods obtaining the camera’s intrinsic and extrinsic parameters. Our approach does not need to use markers and there are no geometrical constraints in the scene either. Thanks to the calculated camera pose it is possible to add virtual objects in the video in a realistic manner.

References

  1. Bouguet, J.-Y., "Pyramidal Implementation of the Lucas Kanade Feature Tracker", Intel Corporation, Technical Report 2000.
  2. Cornelis, K., "From uncalibrated video to augmented reality": Katholike Universiteit Leuven, 2004.
  3. DeMenthon, D. & Davis, L., (1995). Model-Based Object Pose in 25 lines of code. International Journal of Computer Vision, 15, Pp. 123-141.
  4. Hartley, R. & Zisserman, A., (2000). Multiple View Geometry in computer vision: Cambridge University Press.
  5. Kalman, R., (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82, Pp. 35-45.
  6. Kato, H. & Billinghurst, M., (1999). Marker Tracking and HMD Calibration for a video-based Augmented Reality Conferencing System. In International Workshop on Augmented Reality (IWAR), Pp. 85-94. San Francisco, USA.
  7. Mendonca, P. & Cipolla, R., (1999). A simple technique for self-calibration. In IEEE Conference on Computer Vision and Pattern Recognition, Pp. 112-116. Fort Collins, Colorado.
  8. Milgram, P., Takemura, H., Utsumi, A., & Kishino, F., (1994). Augmented Reality: A Class of Displays of the Reality-Virtuality Continuum. In SPIE Conference on Telemanipulator and Telepresence Technologies, Pp. 282-292. Boston, USA, October 31 - November 4.
  9. Shi, J. & Tomasi, C., (1994). Good Features To Track. In IEEE Conference on Computer Vision and Pattern Recognition, Pp. 593-600. Seattle, Washington.
  10. Torr, P., "A Structure and Motion Toolkit in Matlab", Microsoft Research, Cambridge, UK, Technical Report MSR-TR-2002-56, 2002.
Download


Paper Citation


in Harvard Style

Sánchez J. and Borro D. (2007). AUTOMATIC AUGMENTED VIDEO CREATION FOR MARKERLESS ENVIRONMENTS . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 978-972-8865-74-0, pages 519-522. DOI: 10.5220/0002047805190522


in Bibtex Style

@conference{visapp07,
author={J. Sánchez and D. Borro},
title={AUTOMATIC AUGMENTED VIDEO CREATION FOR MARKERLESS ENVIRONMENTS},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2007},
pages={519-522},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002047805190522},
isbn={978-972-8865-74-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - AUTOMATIC AUGMENTED VIDEO CREATION FOR MARKERLESS ENVIRONMENTS
SN - 978-972-8865-74-0
AU - Sánchez J.
AU - Borro D.
PY - 2007
SP - 519
EP - 522
DO - 10.5220/0002047805190522