  There have been a number of corner detection methods proposed for event
cameras in the last years, since event-driven computer vision has become more
accessible. Current state-of-the-art have either unsatisfactory accuracy or
real-time performance when considered for practical use, for example when a
camera is randomly moved in an unconstrained environment. In this paper, we
present yet another method to perform corner detection, dubbed look-up
event-Harris (luvHarris), that employs the Harris algorithm for high accuracy
but manages an improved event throughput. Our method has two major
contributions, 1. a novel "threshold ordinal event-surface" that removes
certain tuning parameters and is well suited for Harris operations, and 2. an
implementation of the Harris algorithm such that the computational load per
event is minimised and computational heavy convolutions are performed only
"as-fast-as-possible", i.e. only as computational resources are available. The
result is a practical, real-time, and robust corner detector that runs more
than 2.6x the speed of current state-of-the-art; a necessity when using
high-resolution event-camera in real-time. We explain the considerations taken
for the approach, compare the algorithm to current state-of-the-art in terms of
computational performance and detection accuracy, and discuss the validity of
the proposed approach for event cameras.
