  We propose a learning approach to corner detection for event-based cameras
that is stable even under fast and abrupt motions. Event-based cameras offer
high temporal resolution, power efficiency, and high dynamic range. However,
the properties of event-based data are very different compared to standard
intensity images, and simple extensions of corner detection methods designed
for these images do not perform well on event-based data. We first introduce an
efficient way to compute a time surface that is invariant to the speed of the
objects. We then show that we can train a Random Forest to recognize events
generated by a moving corner from our time surface. Random Forests are also
extremely efficient, and therefore a good choice to deal with the high capture
frequency of event-based cameras ---our implementation processes up to 1.6Mev/s
on a single CPU. Thanks to our time surface formulation and this learning
approach, our method is significantly more robust to abrupt changes of
direction of the corners compared to previous ones. Our method also naturally
assigns a confidence score for the corners, which can be useful for
postprocessing. Moreover, we introduce a high-resolution dataset suitable for
quantitative evaluation and comparison of corner detection methods for
event-based cameras. We call our approach SILC, for Speed Invariant Learned
Corners, and compare it to the state-of-the-art with extensive experiments,
showing better performance.
