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There have been a number of corner detection methods proposed for eventcameras in the last years, since event-driven computer vision has become moreaccessible. Current state-of-the-art have either unsatisfactory accuracy orreal-time performance when considered for practical use, for example when acamera is randomly moved in an unconstrained environment. In this paper, wepresent yet another method to perform corner detection, dubbed look-upevent-Harris (luvHarris), that employs the Harris algorithm for high accuracybut manages an improved event throughput. Our method has two majorcontributions, 1. a novel "threshold ordinal event-surface" that removescertain tuning parameters and is well suited for Harris operations, and 2. animplementation of the Harris algorithm such that the computational load perevent is minimised and computational heavy convolutions are performed only"as-fast-as-possible", i.e. only as computational resources are available. Theresult is a practical, real-time, and robust corner detector that runs morethan 2.6x the speed of current state-of-the-art; a necessity when usinghigh-resolution event-camera in real-time. We explain the considerations takenfor the approach, compare the algorithm to current state-of-the-art in terms ofcomputational performance and detection accuracy, and discuss the validity ofthe proposed approach for event cameras.

LuvHarris: A Practical Corner Detector for Event-cameras