Dense motion estimation for smoke

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Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.

Original languageEnglish
Title of host publicationComputer Vision -ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers
EditorsK. Nishino, S-H. Lai, V. Lepetit, Y. Sato
PublisherSpringer Nature
Number of pages15
ISBN (Electronic)978-3-319-54190-7
ISBN (Print)9783319541891
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


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