  Because of their high temporal resolution, increased resilience to motion
blur, and very sparse output, event cameras have been shown to be ideal for
low-latency and low-bandwidth feature tracking, even in challenging scenarios.
Existing feature tracking methods for event cameras are either handcrafted or
derived from first principles but require extensive parameter tuning, are
sensitive to noise, and do not generalize to different scenarios due to
unmodeled effects. To tackle these deficiencies, we introduce the first
data-driven feature tracker for event cameras, which leverages low-latency
events to track features detected in a grayscale frame. We achieve robust
performance via a novel frame attention module, which shares information across
feature tracks. By directly transferring zero-shot from synthetic to real data,
our data-driven tracker outperforms existing approaches in relative feature age
by up to 120% while also achieving the lowest latency. This performance gap is
further increased to 130% by adapting our tracker to real data with a novel
self-supervision strategy.
