Research

Dense motion estimation for smoke


Reference:

Chen, D., Li, W. and Hall, P., 2017. Dense motion estimation for smoke. In: Nishino, K., Lai, S.-H., Lepetit, V. and Sato, Y., eds. Computer Vision -ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers. Springer Verlag, pp. 225-239. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 10114)

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Official URL:

http://dx.doi.org/10.1007/978-3-319-54190-7_14

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Abstract

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.

Details

Item Type Book Sections
CreatorsChen, D., Li, W. and Hall, P.
EditorsNishino, K., Lai, S.-H., Lepetit, V. and Sato, Y.
DOI10.1007/978-3-319-54190-7_14
Related URLs
URLURL Type
http://www.scopus.com/inward/record.url?scp=85016029858&partnerID=8YFLogxKUNSPECIFIED
DepartmentsFaculty of Science > Computer Science
StatusPublished
ID Code55534

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