Lucas-Kanade Optical Flow Machine Learning Implementations
DOI:
https://doi.org/10.47611/jsrhs.v11i3.2957Keywords:
Optical Flow, Machine Learning, CNN, Lucas-Kanade, Algorithm, Computer VisionAbstract
Optical flow is an effective measurement to gauge motion in a scene, which allows for the computation of pixel-by-pixel motion in a frame pair. This paper aims to address the ambiguity with determining how to gain optical flow results for a given sequence. Due to varying speeds and nuances of a sequence, where it’s set, how fast it’s moving, a different amount of blur radius, i.e., the extent to which the image is blurred, may have to be applied to gain realistic flow maps. Furthermore, this paper touches on the many variables that can impact the efficacy of the flow outputted by an optical flow algorithm. Thus, we aim to determine whether the composition of results obtained through different blur values provides for more ground-truth flow outputs.
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Barron, John L.; Fleet, David J. & Beauchemin, Steven (1994). "Performance of optical flow techniques" (PDF). International Journal of Computer Vision. 12: 43–77. CiteSeerX 10.1.1.173.481. doi:10.1007/bf01420984. S2CID 1290100.
Beauchemin, S. S.; Barron, J. L. (1995). "The computation of optical flow". ACM Computing Surveys. ACM New York, USA. 27 (3): 433–466. doi:10.1145/212094.212141. S2CID 1334552.
Fleet, David J.; Weiss, Yair (2006). "Optical Flow Estimation" (PDF). In Paragios, Nikos; Chen, Yunmei; Faugeras, Olivier D. (eds.). Handbook of Mathematical Models in Computer Vision. Springer. pp. 237–257. ISBN 978-0-387-26371-7.
3. The Gaussian Kernel - University of Wisconsin–Madison. http://pages.stat.wisc.edu/~mchung/teaching/MIA/reading/diffusion.gaussian.kernel.pdf.
B. D. Lucas and T. Kanade (1981), An iterative image registration technique with an application to stereo vision. Proceedings of Imaging Understanding Workshop, pages 121--130
B.K.P. Horn and B.G. Schunck, "Determining optical flow." Artificial Intelligence, vol 17, pp 185–203, 1981.
Fischer, Philipp, et al. “FlowNet: Learning Optical Flow with Convolutional Networks.” ArXiv.org, 4 May 2015, https://arxiv.org/abs/1504.06852.
“Gaussian Blur.” Gaussian Blur - an Overview | ScienceDirect Topics, https://www.sciencedirect.com/topics/engineering/gaussian-blur.
Horn, Berthold K.P.; Schunck, Brian G. (August 1981). "Determining optical flow" (PDF). Artificial Intelligence. 17 (1–3): 185–203. doi:10.1016/0004-3702(81)90024-2. hdl:1721.1/6337.
Lecture 30: Video Tracking: Lucas-Kanade. http://www.cse.psu.edu/~rtc12/CSE486/lecture30.pdf
Lin, Chuan-en. “Introduction to Motion Estimation with Optical Flow.” AI & Machine Learning Blog, AI & Machine Learning Blog, 24 Apr. 2020, https://nanonets.com/blog/optical-flow/.
Lucas-Kanade in a Nutshell - Fu-Berlin.de. http://www.inf.fu-berlin.de/inst/ag-ki/rojas_home/documents/tutorials/Lucas-Kanade2.pdf.
Lucas-Kanade Optical Flow - Carnegie Mellon School of ... http://www.cs.cmu.edu/~16385/s15/lectures/Lecture21.pdf.
Motion and Form Tutorial, http://web.mit.edu/persci/demos/Motion&Form/master.html.
Motion and Optical Flow - Electrical Engineering and ... https://web.eecs.umich.edu/~jjcorso/t/598F14/files/lecture_1015_motion.pdf.
Motion and Optical Flow - Homes.cs.washington.edu. https://homes.cs.washington.edu/~shapiro/EE596/notes/Optical_Flow.pdf.
Motion Fields - University of Washington. https://grail.cs.washington.edu/projects/motion-fields/motion-fields.pdf.
Motion Fields Are Hardly Ever Ambiguous - People.csail.mit.edu.
https://people.csail.mit.edu/bkph/papers/Motion_Fields-OPT.pdf.
Moving Camera Lecture 22: Camera Motion. http://www.cse.psu.edu/~rtc12/CSE486/lecture22_6pp.pdf.
MPI Sintel Dataset. http://sintel.is.tue.mpg.de/.
Optic Flow, https://isle.hanover.edu/Ch07DepthSize/Ch07Optic%20Flow.html.
Optical Flow Estimation - Department of Computer Science ... https://www.cs.toronto.edu/~fleet/research/Papers/flowChapter05.pdf.
Optical Flow Measurement Using Lucas ... - Ijcaonline.org. https://research.ijcaonline.org/volume61/number10/pxc3884611.pdf.
“Optical Flow.” Optical Flow - an Overview | ScienceDirect Topics, https://www.sciencedirect.com/topics/engineering/optical-flow#:~:text=4.1%20Optical%20Flow-,Optical%20flow%20is%20defined%20as%20the%20apparent%20motion%20of%20individual,projected%20onto%20the%20image%20plane.
Optical Flow: Horn-Schunck. http://www.cs.cmu.edu/~16385/s17/Slides/14.3_OF__HornSchunck.pdf.
Optical Flow: Techniques and Applications. https://www.dgp.toronto.edu/~donovan/stabilization/opticalflow.pdf.
“Optical Flow.” OpenCV, https://docs.opencv.org/3.4/d4/dee/tutorial_optical_flow.html.
“The Role of Probability.” Central Limit Theorem, https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_probability/BS704_Probability12.html#:~:text=The%20central%20limit%20theorem%20states,will%20be%20approximately%20normally%20distributed.
State Estimation Using Optical ... - University of Florida. https://mae.ufl.edu/ricklind/rick_pro/of/GNC06_OF.pdf.
Synced. “A Brief Review of FlowNet.” Medium, Towards Data Science, 11 Sept. 2017, https://towardsdatascience.com/a-brief-review-of-flownet-dca6bd574de0.
Takeo Kanade - The Robotics Institute Carnegie Mellon ... https://www.ri.cmu.edu/ri-faculty/takeo-kanade/.
University of Alberta Dictionary of Cognitive Science: Aperture Problem, http://www.bcp.psych.ualberta.ca/~mike/Pearl_Street/Dictionary/contents/A/aperture.html.
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