References
- Bradski G. The OpenCV Library. Dr Dobb’s Journal of Software Tools, 2000; 25: 120–125.
- Butler DJ, Wulff J, Stanley GB, Black MJ. A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon A, et al (Eds.), European Conf. on Computer Vision (ECCV),
Springer-Verlag , 2012; 611–625. DOI: 10.1007/978-3-642-33783-3_44 - Butler DJ, Wulff J, Stanley GB, Black MJ. 2021. URL
http://sintel.is.tue.mpg.de/results , accessed: 11/06/21. - Farnebäck G. Two-frame motion estimation based on polynomial expansion. In: Scandinavian conference on Image analysis,
Springer , 2003; 363–370. DOI: 10.1007/3-540-45103-X_50 - Harris CR, Millman KJ, van derWalt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, et al. Array programming with numpy. Nature, 2020; 585(7825): 357–362. DOI: 10.1038/s41586-020-2649-2
- Horn BK, Schunck BG. Determining optical flow. Artificial intelligence, 1981; 17(1–3): 185–203. DOI: 10.1016/0004-3702(81)90024-2
- Ilg E, Mayer N, Saikia T, Keuper M, Dosovitskiy A, Brox T. Flownet 2.0: Evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017; 2462–2470. DOI: 10.1109/CVPR.2017.179
- Liu C, et al.
Beyond pixels: exploring new representations and applications for motion analysis . PhD thesis, Massachusetts Institute of Technology; 2009. - Lucas BD, Kanade T. An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, 1981; 674–679.
- Menze M, Heipke C, Geiger A. Object scene flow. ISPRS Journal of Photogrammetry and Remote Sensing, 2018; 140: 60–76. DOI: 10.1016/j.isprsjprs.2017.09.013
- NVIDIA. 2020. URL
https://docs.nvidia.com/cuda/ , accessed: 11/06/21 - Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 2019; 32: 8024–8035.
- Pathak D, Girshick R, Dollár P, Darrell T, Hariharan B. Learning features by watching objects move. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017; 2701–2710. DOI: 10.1109/CVPR.2017.638
- Pont-Tuset J, Perazzi F, Caelles S, Arbeláez P, Sorkine-Hornung A, Van Gool L. The 2017 DAVIS challenge on video object segmentation; 2017. arXiv preprint arXiv:170400675.
- Ravasio CS, Pissas T, Bloch E, Flores B, Jalali S, Stoyanov D, Cardoso JM, Da Cruz L, Bergeles C. Learned optical flow for intra-operative tracking of the retinal fundus. International journal of computer assisted radiology and surgery, 2020; 15(5): 827–836. DOI: 10.1007/s11548-020-02160-9
- Royo D. flowvid: Optical flow video tools; 2021. URL
https://pypi.org/project/flowvid/ , accessed: 11/06/21. - Runia T. flow-vis: Easy optical flow visualisation in python; 2021. URL
https://pypi.org/project/flow-vis/ , accessed: 11/06/21. - Seznec M. flowpy: Tools for working with optical flow; 2021. URL
https://pypi.org/project/flowpy/ , accessed: 11/06/21. - Sun D, Yang X, Liu MY, Kautz J. PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018; 8934–8943. DOI: 10.1109/CVPR.2018.00931
- Teed Z, Deng J. RAFT: Recurrent all-pairs field transforms for optical flow. In: European Conference on Computer Vision,
Springer , 2020; 402–419. DOI: 10.1007/978-3-030-58536-5_24 - Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, et al. Scipy 1.0: fundamental algorithms for scientific computing in python. Nature methods, 2020; 17(3): 261–272. DOI: 10.1038/s41592-019-0686-2