  In this work, we propose a novel transformation for events from an event
camera that is equivariant to optical flow under convolutions in the 3-D
spatiotemporal domain. Events are generated by changes in the image, which are
typically due to motion, either of the camera or the scene. As a result,
different motions result in a different set of events. For learning based tasks
based on a static scene such as classification which directly use the events,
we must either rely on the learning method to learn the underlying object
distinct from the motion, or to memorize all possible motions for each object
with extensive data augmentation. Instead, we propose a novel transformation of
the input event data which normalizes the $x$ and $y$ positions by the
timestamp of each event. We show that this transformation generates a
representation of the events that is equivariant to this motion when the
optical flow is constant, allowing a deep neural network to learn the
classification task without the need for expensive data augmentation. We test
our method on the event based N-MNIST dataset, as well as a novel dataset
N-MOVING-MNIST, with significantly more variety in motion compared to the
standard N-MNIST dataset. In all sequences, we demonstrate that our transformed
network is able to achieve similar or better performance compared to a network
with a standard volumetric event input, and performs significantly better when
the test set has a larger set of motions than seen at training.
