  Event cameras are novel neuromorphic vision sensors with ultrahigh temporal
resolution and low latency, both in the order of microseconds. Instead of image
frames, event cameras generate an asynchronous event stream of per-pixel
intensity changes with precise timestamps. The resulting sparse data structure
impedes applying many conventional computer vision techniques to event streams,
and specific algorithms should be designed to leverage the information provided
by event cameras. We propose a corner detection algorithm, eSUSAN, inspired by
the conventional SUSAN (smallest univalue segment assimilating nucleus)
algorithm for corner detection. The proposed eSUSAN extracts the univalue
segment assimilating nucleus from the circle kernel based on the similarity
across timestamps and distinguishes corner events by the number of pixels in
the nucleus area. Moreover, eSUSAN is fast enough to be applied to CeleX-V, the
event camera with the highest resolution available. Based on eSUSAN, we also
propose the SE-Harris corner detector, which uses adaptive normalization based
on exponential decay to quickly construct a local surface of active events and
the event-based Harris detector to refine the corners identified by eSUSAN. We
evaluated the proposed algorithms on a public dataset and CeleX-V data. Both
eSUSAN and SE-Harris exhibit higher real-time performance than existing
algorithms while maintaining high accuracy and tracking performance.
