Abstract
We present a novel algorithm for improving the accuracy of structure from motion on video sequences. Its goal is to efficiently recover scene structure and camera pose by using dynamic programming to maximize the lengths of putative keypoint tracks. By efficiently discarding poor correspondences while maintaining the largest possible set of inliers, it ultimately provides a robust and accurate scene reconstruction. Traditional outlier detection strategies, such as RANSAC and its derivatives, cannot handle high dimensional problems such as structure from motion over long image sequences. We prove that, given an estimate of the camera pose at a given frame, the outlier detection is optimal and runs in low order polynomial time. The algorithm is applied on-line, processing each frame in sequential order. Results are presented on several indoor and outdoor video sequences processed both with and without the proposed optimization. The improvement in average reprojection errors demonstrates its effectiveness.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, New York (2003)
Pollefeys, M., Van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. International Journal of Computer Vision 59(3), 207–232 (2004)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. In: SIGGRAPH 2006: ACM SIGGRAPH 2006 Papers, pp. 835–846 (2006)
Fitzgibbon, A.W., Zisserman, A.: Automatic camera recovery for closed or open image sequences. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 311–326. Springer, Heidelberg (1998)
Meltzer, J., Soatto, S.: Edge descriptors for robust wide-baseline correspondence (2008)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Bouguet, J.Y.: Pyramidal implementation of the Lucas-Kanade feature tracker. OpenCV library (2001), http://sourceforge.net/projects/opencvlibrary
Mooser, J., Wang, Q., You, S., Neumann, U.: Fast simultaneous tracking and recognition by incremental keypoint matching. In: 3D Data Processing, Visualization and Transmission (2008)
Kolmogorov, V., Zabih, R.: Multi-camera scene reconstruction via graph cuts. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 82–96. Springer, Heidelberg (2002)
Gallup, D., Frahm, J.M., Mordohai, P., Yang, Q., Pollefeys, M.: Real-time plane-sweeping stereo with multiple sweeping directions, pp. 1–8 (2007)
Zhu, Z., Oskiper, T., Samarasekera, S., Kumar, R., Sawhney, H.: Real-time global localization with a pre-built visual landmark database (2008)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Torr, P.H.S., Zisserman, A.: Mlesac: a new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78(1), 138–156 (2000)
Nistér, D.: Preemptive ransac for live structure and motion estimation. In: IEEE International Conference on Computer Vision, p. 199 (2003)
Buchanan, A., Fitzgibbon, A.: Interactive feature tracking using k-d trees and dynamic programming. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 626–633 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mooser, J., You, S., Neumann, U., Grasset, R., Billinghurst, M. (2010). A Dynamic Programming Approach to Maximizing Tracks for Structure from Motion. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-12304-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12303-0
Online ISBN: 978-3-642-12304-7
eBook Packages: Computer ScienceComputer Science (R0)