  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.
