The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them
Open accessAbstract
The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.
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Cites
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205 · cited 1×
“The field of optical flow estimation is making steady progress as evidenced by the increasing accuracy of current meth-ods on the Middlebury optical flow benchmark (Baker et al. 2007).”
BackgroundSelf-citationA Naturalistic Open Source Movie for Optical Flow Evaluation
Butler, D. J., Wulff, J., Stanley, G. B., & Black, M. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_44 · Cited by 1,842
Self-citationA Duality Based Approach for Realtime TV-L 1 Optical Flow
Zach, C., Pock, T., & Bischof, H. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74936-3_22 · Cited by 1,658 · cited 5×
“Note in practice the weight λC on the coupling term is usually small or is steadily increased from small values (Wedel et al. 2008b; Zach et al. 2007).”
BackgroundMethodInfluentialFusionFlow: Discrete-continuous optimization for optical flow estimation
Lempitsky, V., Roth, S., & Rother, C. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587751 · Cited by 145 · cited 2×
“…al. 2010; Zimmer et al. 2009) to reduce the influence of lighting changes, incremental warping (Bergen et al. 1992), warping with bicubic interpolation (Lempitsky et al. 2008; Wedel et al. 2008b), temporal averaging of image derivatives (Horn 1986; Wedel et al. 2008b),…”
BackgroundSelf-citationZimmer, H., Bruhn, A., Weickert, J., Valgaerts, L., Salgado, A., Rosenhahn, B., & Seidel, H.-P. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-03641-5_16 · Cited by 107 · cited 6×
“mation to deal with large motions [13,17], texture decomposition [65,67] or high-order filter constancy [8,17,29,39, 77] to reduce the influence of lighting changes, incremental”
BackgroundMethodInfluentialOptical flow estimation on coarse-to-fine region-trees using discrete optimization
Lei, C., & Yang, Y.-H. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459253 · Cited by 67 · cited 1×
“…(Nagel and Enkelmann 1986; Sun et al. 2008; Wedel et al. 2009; Zimmer et al. 2011, 2009), rigidity constraints (Wedel et al. 2008a, 2009), an over-parameterized smoothness term (Nir et al. 2008), or image segmentation (Black and Jepson 1996; Lei and Yang 2009; Xu et al. 2008; Zitnick et al. 2005).”
Background
Cited by
Object scene flow for autonomous vehicles
Menze, M., & Geiger, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298925 · Cited by 2,270
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
Hui, T.-W., Tang, X., & Loy, C. C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00936 · Cited by 817
EpicFlow: Edge-preserving interpolation of correspondences for optical flow
Revaud, J., Weinzaepfel, P., Harchaoui, Z., & Schmid, C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298720 · Cited by 697
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Zhu, A., Yuan, L., Chaney, K., & Daniilidis, K. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.062 · Cited by 470
Unsupervised deep learning for optical flow estimation
Ren, Z., Yan, J., Ni, B., Liu, B., Yang, X., & Zha, H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10723 · Cited by 296
Metadata
- Authors
- Sun, D., Roth, S., & Black, M. J.
- Venue
- International Journal of Computer Vision
- Published
- 2013-09-03
- Volume
- 106
- Issue
- 2
- Pages
- 115-137
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2155302366
- Cited by
- 614
- References
- 8
- FWCI
- 53.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
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