Lucas-Kanade Optical Flow Machine Learning Implementations

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.