  This paper introduces an unsupervised compact architecture that can extract
features and classify the contents of dynamic scenes from the temporal output
of a neuromorphic asynchronous event-based camera. Event-based cameras are
clock-less sensors where each pixel asynchronously reports intensity changes
encoded in time at the microsecond precision. While this technology is gaining
more attention, there is still a lack of methodology and understanding of their
temporal properties. This paper introduces an unsupervised time-oriented
event-based machine learning algorithm building on the concept of hierarchy of
temporal descriptors called time surfaces. In this work we show that the use of
sparse coding allows for a very compact yet efficient time-based machine
learning that lowers both the computational cost and memory need. We show that
we can represent visual scene temporal dynamics with a finite set of elementary
time surfaces while providing similar recognition rates as an uncompressed
version by storing the most representative time surfaces using clustering
techniques. Experiments will illustrate the main optimizations and trade-offs
to consider when implementing the method for online continuous vs. offline
learning. We report results on the same previously published 36 class character
recognition task and a 4 class canonical dynamic card pip task, achieving 100%
accuracy on each.
