Lucas-Kanade Optical Flow Machine Learning Implementations

Authors

  • Vedant Gaur Aragon High School

DOI:

https://doi.org/10.47611/jsrhs.v11i3.2957

Keywords:

Optical Flow, Machine Learning, CNN, Lucas-Kanade, Algorithm, Computer Vision

Abstract

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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References or Bibliography

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Published

08-31-2022

How to Cite

Gaur, V. (2022). Lucas-Kanade Optical Flow Machine Learning Implementations. Journal of Student Research, 11(3). https://doi.org/10.47611/jsrhs.v11i3.2957

Issue

Section

HS Research Projects