  Unlike conventional cameras which capture video at a fixed frame rate,
Dynamic Vision Sensors (DVS) record only changes in pixel intensity values. The
output of DVS is simply a stream of discrete ON/OFF events based on the
polarity of change in its pixel values. DVS has many attractive features such
as low power consumption, high temporal resolution, high dynamic range and
fewer storage requirements. All these make DVS a very promising camera for
potential applications in wearable platforms where power consumption is a major
concern.
  In this paper, we explore the feasibility of using DVS for Human Activity
Recognition (HAR). We propose to use the various slices (such as $x-y$, $x-t$,
and $y-t$) of the DVS video as a feature map for HAR and denote them as Motion
Maps. We show that fusing motion maps with Motion Boundary Histogram (MBH) give
good performance on the benchmark DVS dataset as well as on a real DVS gesture
dataset collected by us. Interestingly, the performance of DVS is comparable to
that of conventional videos although DVS captures only sparse motion
information.
