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Improving the Optical Flow Accuracy Based on the Total Variation of Local-Global method

  • Yugui Zhang
  • , Haonan Fan
  • , Jin Zheng*
  • , Chi Zhang
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The aim of this paper is to improve the optical flow accuracy for the preservation of flow discontinuities in total variation method. The proposed method is based on the total variation of the local-global method, including the following steps: firstly, the initial optical flow is obtained from the local-global method; secondly, the data item and the smoothing item are designed based on initial optical flow by using the total variation, with the data item involving the brightness constancy and the gradient constancy, and the smoothing item involving exponential function; and finally, the optical flow results from the second step are optimized by using median filtering. The experimental results show that our proposed method could enhance the accuracy and robustness of the optical flow on the Middlebury Dataset and the MPI Sintel Dataset.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsNaoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4658-4664
Number of pages7
ISBN (Electronic)9781538650356
DOIs
StatePublished - 2 Jul 2018
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: 10 Dec 201813 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
Country/TerritoryUnited States
CitySeattle
Period10/12/1813/12/18

Keywords

  • Combined local-global
  • Median filtering
  • Optical flow estimation
  • Total variation

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