Abstract
Tracking through long image sequences is a fundamental research issue in computer vision. This task relies on estimating correspondences between image pairs over time where error accumulation in tracking can result in drift. In this paper, we propose an optimization framework that utilises a novel Anchor Patch algorithm which significantly reduces overall tracking errors given long sequences containing highly deformable objects. The framework may be applied to any tracking algorithm that calculates dense correspondences between images, e.g. optical flow. We demonstrate the success of our approach by showing significant tracking error reduction using 6 existing optical flow algorithms applied to a range of benchmark ground truth sequences. We also provide quantitative analysis of our approach given synthetic occlusions and image noise.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
DeCarlo, D., Metaxas, D.: The integration of optical flow and deformable models with applications to human face shape and motion estimation. In: Computer Vision and Pattern Recognition (CVPR), pp. 231–238. IEEE (1996)
Borshukov, G., Piponi, D., Larsen, O., Lewis, J., Tempelaar-Lietz, C.: Universal capture: image-based facial animation for the matrix reloaded. In: ACM SIGGRAPH 2005 Courses, p. 16. ACM (2005)
Ezzat, T., Geiger, G., Poggio, T.: Trainable videorealistic speech animation. ACM Transactions on Graphics (TOG) 21, 388–398 (2002)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Brox, T., Malik, J.: Large displacement optical flow: Descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 500–513 (2011)
Bradley, D., Heidrich, W., Popa, T., Sheffer, A.: High resolution passive facial performance capture. ACM Transactions on Graphics (TOG) 29, 41 (2010)
Beeler, T., Hahn, F., Bradley, D., Bickel, B., Beardsley, P., Gotsman, C., Sumner, R.W., Gross, M.: High-quality passive facial performance capture using anchor frames. ACM Transactions on Graphics (TOG) 30, 75 (2011)
Garg, R., Roussos, A., Agapito, L.: Robust trajectory-space tv-l1 optical flow for non-rigid sequences. Energy Minimazation Methods in Computer Vision and Pattern Recognition, 300–314 (2011)
Sun, D., Roth, S., Black, M.: Secrets of optical flow estimation and their principles. In: Computer Vision and Pattern Recognition (CVPR), pp. 2432–2439 (2010)
Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An improved algorithm for tv-l1 optical flow. Statistical and Geometrical Approaches to Visual Motion Analysis, 23–45 (2009)
Pizarro, D., Bartoli, A.: Feature-based deformable surface detection with self-occlusion reasoning. International Journal of Computer Vision, 1–17 (2012)
Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable libraryof computer vision algorithms (2008)
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. International Journal of Computer Vision 92, 1–31 (2011)
Drulea, M., Nedevschi, S.: Total variation regularization of local-global optical flow. In: Intelligent Transportation Systems (ITSC), pp. 318–323. IEEE (2011)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial intelligence 17, 185–203 (1981)
Black, M.J., Anandan, P.: The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding 63, 75–104 (1996)
White, R., Crane, K., Forsyth, D.A.: Capturing and animating occluded cloth. ACM Transactions on Graphics (TOG) 26, 34 (2007)
Salzmann, M., Hartley, R., Fua, P.: Convex optimization for deformable surface 3-d tracking. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, W., Cosker, D., Brown, M. (2013). An Anchor Patch Based Optimization Framework for Reducing Optical Flow Drift in Long Image Sequences. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-37431-9_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37430-2
Online ISBN: 978-3-642-37431-9
eBook Packages: Computer ScienceComputer Science (R0)