  We introduce the concept of "dynamic image", a novel compact representation
of videos useful for video analysis, particularly in combination with
convolutional neural networks (CNNs). A dynamic image encodes temporal data
such as RGB or optical flow videos by using the concept of `rank pooling'. The
idea is to learn a ranking machine that captures the temporal evolution of the
data and to use the parameters of the latter as a representation. When a linear
ranking machine is used, the resulting representation is in the form of an
image, which we call dynamic because it summarizes the video dynamics in
addition of appearance. This is a powerful idea because it allows to convert
any video to an image so that existing CNN models pre-trained for the analysis
of still images can be immediately extended to videos. We also present an
efficient and effective approximate rank pooling operator, accelerating
standard rank pooling algorithms by orders of magnitude, and formulate that as
a CNN layer. This new layer allows generalizing dynamic images to dynamic
feature maps. We demonstrate the power of the new representations on standard
benchmarks in action recognition achieving state-of-the-art performance.
