Video Acceleration Magnification

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

60 Citations (Scopus)
35 Downloads (Pure)


The ability to amplify or reduce subtle image changes over time is useful in contexts such as video editing, medical video analysis, product quality control and sports. In these contexts there is often large motion present which severely distorts current video amplification methods that magnify change linearly. In this work we propose a method to cope with large motions while still magnifying small changes. We make the following two observations: i) large motions are linear on the temporal scale of the small changes, ii) small changes deviate from this linearity. We ignore linear motion and propose to magnify acceleration. Our method is pure Eulerian and does not require any optical flow, temporal alignment or region annotations. We link temporal second-order derivative filtering to spatial acceleration magnification. We apply our method to moving objects where we show motion magnification and color magnification. We provide quantitative as well as qualitative evidence for our method while comparing to the state-of-the-art.
Original languageEnglish
Title of host publication2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
EditorsL. O'Conner
Place of PublicationPiscataway
Number of pages9
ISBN (Electronic)978-1-5386-0457-1
ISBN (Print)978-1-5386-0458-8
Publication statusPublished - 2017
Event30th IEEE Conference on Computer Vision and Pattern Recognition - Honolulu, United States
Duration: 21 Jul 201726 Jul 2017


Conference30th IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVRP 2017
Country/TerritoryUnited States


  • Acceleration
  • Cameras
  • Tracking
  • Feature extraction
  • Laplace equations
  • Linearity
  • Taylor series


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