Real-Time Estimation of Camera Orientation by Tracking Orthogonal Vanishing Points in Videos

Wael Elloumi, Sylvie Treuillet, Rémy Leconge

2013

Abstract

In man-made urban environments, vanishing points are pertinent visual cues for navigation task. But estimating the orientation of an embedded camera relies on the ability to find a reliable triplet of orthogonal vanishing points in real-time. Based on previous works, we propose a pipeline to achieve an accurate estimation of the camera orientation while preserving a short processing time. Our algorithm pipeline relies on two contributions: a novel sampling strategy among finite and infinite vanishing points extracted with a RANSAC-based line clustering and a tracking along a video sequence to enforce the accuracy and the robustness by extracting the three most pertinent orthogonal directions. Experiments on real images and video sequences show that the proposed strategy for selecting the triplet of vanishing points is pertinent as our algorithm gives better results than the recently published RNS optimal method (Mirzaei, 2011), in particular for the yaw angle, which is actually essential for navigation task.

References

  1. M. Antone and S. Teller, 2000. Automatic recovery of relative camera rotations for urban scene. In: Proc. of IEEE Conf. Computer Vision and Pattern recognition (CVPR) 282-289.
  2. S. T. Barnard, 1983. Interpreting perspective images. Artificial Intelligence, 21(4), 435-462, Elsevier Science B.V.
  3. J. C. Bazin, Y. Seo, C. Demonceaux, P. Vasseur, K. Ikeuchi, I. Kweon and M. Pollefeys, 2012. Globally optimal line clustering and vanishing points estimation in a Manhattan world. In: the IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR).
  4. K. Boulanger, K. Bouatouch, and S. Pattanaik, 2006. ATIP : A tool for 3D navigation inside a single image with automatic camera calibration. In: EG UK Conf on Theory and Practice of Computer Graphics.
  5. V. Cantoni, L. Lombardi, M. Porta and N. Sicard, 2001. Vanishing Point Detection: Representation Analysis and New Approaches. In : Proc. of Int. Conf. on Image Analysis and Processing (ICIAP), 90-94.
  6. R. T Collins and R. S Weiss, 1990. Vanishing point calculation as statistical inference on the unit sphere. In: Proceedings of the 3rd Int. Conference on Computer Vision (ICCV), 400-403.
  7. J. M. Coughlan and A. L. Yuille, 1999. Manhattan World: Compass direction from a single image by Bayesian inference. In: Int. Conference on Computer Vision (ICCV).
  8. P. Denis, J. H. Elder and F. Estrada, 2008. Efficient EdgeBased Methods for Estimating Manhattan Frames in Urban Imagery. In: European Conference on Computer Vision (ECCV), 197-210.
  9. W. Förstner, 2010. Optimal vanishing point detection and rotation estimation of single images from a legoland scene. In: Proceedings of the ISPRS Symposium Commision III PCV. S. 157-163, Part A, Paris.
  10. M. Kalantari, A. Hashemi, F. Jung and J.P. Guédon, 2011. A New Solution to the Relative Orientation Problem Using Only 3 Points and the Vertical Direction. Journal of Mathematical Imaging and Vision archive Volume 39(3).
  11. J. Kosecka and W. Zhang, 2002, Video Compass, In Proc. of the 7th European Conf. on Computer Vision (ECCV).
  12. A. Martins, P. Aguiar and M. Figueiredo, 2005. Orientation in Manhattan world: Equiprojective classes and sequential estimation. In: the IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 27, 822-826.
  13. F. M. Mirzaei and S. I. Roumeliotis, 2011. Optimal estimation of vanishing points in a Manhattan world. In: the Proc. of IEEE Int. Conf. on Computer Vision (ICCV).
  14. M. Nieto and L. Salgado, 2011. Simultaneous estimation of vanishing points and their converging lines using the EM algorithm. Pattern Recognition Letters, vol. 32(14), 1691-1700.
  15. R. Pflugfelder and Bischof, 2005. Online auto-calibration in man-made world. In: Proc. Digital Image Computing : Techniques and Applications, 519-526.
  16. C. Rother, 2000. A new approach for vanishing point detection in architectural environments. In: Proc. of the 11th British Machine Vision Conference (BMVC), 382-391.
  17. J.-P. Tardif, 2009. Non-iterative approach for fast and accurate vanishing point detection. In: Proc. Int. Conference on Computer Vision (ICCV), 1250-1257.
Download


Paper Citation


in Harvard Style

Elloumi W., Treuillet S. and Leconge R. (2013). Real-Time Estimation of Camera Orientation by Tracking Orthogonal Vanishing Points in Videos . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 215-222. DOI: 10.5220/0004214102150222


in Bibtex Style

@conference{visapp13,
author={Wael Elloumi and Sylvie Treuillet and Rémy Leconge},
title={Real-Time Estimation of Camera Orientation by Tracking Orthogonal Vanishing Points in Videos },
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={215-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004214102150222},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Real-Time Estimation of Camera Orientation by Tracking Orthogonal Vanishing Points in Videos
SN - 978-989-8565-48-8
AU - Elloumi W.
AU - Treuillet S.
AU - Leconge R.
PY - 2013
SP - 215
EP - 222
DO - 10.5220/0004214102150222