User:Moderately Sized Greg/sandbox: Difference between revisions

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== Estimation ==

Optical flow can be estimated in a number of ways. Broadly, optical flow estimation approaches can be divided into machine learning- based models (sometimes called data-driven models), classical models (sometimes called knowledge-driven models) which do not use machine learning and hybrid models which use aspects of both learning based models and classical models.<ref name="Zhai_Survey_2021">{{cite journal |last1=Zhai |first1=Mingliang |last2=Xiang |first2=Xuezhi |last3=Lv |first3=Ning |last4=Kong |first4=Xiangdong |title=Optical flow and scene flow estimation: A survey |journal=Pattern Recognition |date=2021 |volume=114 |pages=107861 |doi=https://doi.org/10.1016/j.patcog.2021.107861 |url=https://www.sciencedirect.com/science/article/pii/S0031320321000480}}</ref>

===Classical Models===

Many classical models use the intuitive assumption of ''brightness constancy''; that even if a point moves between frames, its brightness stays constant. <ref name="Fortun_Survey_2015">{{cite journal |last1=Fortun |first1=Denis |last2=Bouthemy |first2=Patrick |last3=Kervrann |first3=Charles|title=Optical flow modeling and computation: A survey |journal=Computer Vision and Image Understanding |date=2015-05-01 |volume=134 |pages=1-21 |doi=10.1016/j.cviu.2015.02.008 |url=https://www.sciencedirect.com/science/article/pii/S1077314215000429 |access-date=2024-12-23}}</ref> To formalise this intuitive assumption, consider two consecutive frames from a video sequence, with intensity <math>I(x, y, t)</math>, where <math>(x, y)</math> refer to pixel coordinates and <math>t</math> refers to time. In this case, the brightness constancy constraint is

:<math>

I(x, y, t) = I(x + u, y + v, t + 1),

</math>

where <math>\mathbf{w}:= (u, v, 1)</math> is the displacement vector between a point in the first frame and the corresponding point in the second frame.<ref name="Brox_2004">{{cite conference |url=http://link.springer.com/10.1007/978-3-540-24673-2_3 |title=High Accuracy Optical Flow Estimation Based on a Theory for Warping |last1=Brox |first1=Thomas |last2=Bruhn |first2=Andrés |last3=Papenberg |first3=Nils |last3=Weickert |first3=Joachim |date=2004 |publisher=Springer Berlin Heidelberg |book-title=Computer Vision - ECCV 2004 |pages=25-36 |___location=Berlin, Heidelberg |conference=ECCV 2004}}</ref>

===Learning Based Models===