Using Phase Instead of Optical Flow for Action Recognition

Omar Hommos, Silvia L. Pintea*, Pascal S.M. Mettes, Jan C. van Gemert

*Corresponding author for this work

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

4 Citations (Scopus)
75 Downloads (Pure)


Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dynamics. In contrast to the Lagrangian perspective, the Eulerian model of dynamics does not track, but describes local changes. For video, an Eulerian phase-based motion representation, using complex steerable filters, has been successfully employed recently for motion magnification and video frame interpolation. Inspired by these previous works, here, we proposes learning Eulerian motion representations in a deep architecture for action recognition. We learn filters in the complex domain in an end-to-end manner. We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction. We propose a phase-information extraction module, based on these complex filters, that can be used in any network architecture for extracting Eulerian representations. We experimentally analyze the added value of Eulerian motion representations, as extracted by our proposed phase extraction module, and compare with existing motion representations based on optical flow, on the UCF101 dataset.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 Workshops, Proceedings
EditorsLaura Leal-Taixé, Stefan Roth
Place of PublicationCham
Number of pages14
Editionpart VI
ISBN (Electronic)978-3-030-11024-6
ISBN (Print)978-303011023-9
Publication statusPublished - 2019
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 8 Sept 201814 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11134 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th European Conference on Computer Vision, ECCV 2018

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Action recognition
  • Eulerian motion representation
  • Motion representation
  • Phase derivatives


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