  We present a novel end-to-end approach to keypoint detection and tracking in
an event stream that provides better precision and much longer keypoint tracks
than previous methods. This is made possible by two contributions working
together.
  First, we propose a simple procedure to generate stable keypoint labels,
which we use to train a recurrent architecture. This training data results in
detections that are very consistent over time.
  Moreover, we observe that previous methods for keypoint detection work on a
representation (such as the time surface) that integrates events over a period
of time. Since this integration is required, we claim it is better to predict
the keypoints' trajectories for the time period rather than single locations,
as done in previous approaches. We predict these trajectories in the form of a
series of heatmaps for the integration time period. This improves the keypoint
localization.
  Our architecture can also be kept very simple, which results in very fast
inference times. We demonstrate our approach on the HVGA ATIS Corner dataset as
well as "The Event-Camera Dataset and Simulator" dataset, and show it results
in keypoint tracks that are three times longer and nearly twice as accurate as
the best previous state-of-the-art methods. We believe our approach can be
generalized to other event-based camera problems, and we release our source
code to encourage other authors to explore it.
