  This work addresses the issue of motion compensation and pattern tracking in
event camera data. An event camera generates asynchronous streams of events
triggered independently by each of the pixels upon changes in the observed
intensity. Providing great advantages in low-light and rapid-motion scenarios,
such unconventional data present significant research challenges as traditional
vision algorithms are not directly applicable to this sensing modality. The
proposed method decomposes the tracking problem into a local SE(2)
motion-compensation step followed by a homography registration of small
motion-compensated event batches. The first component relies on Gaussian
Process (GP) theory to model the continuous occupancy field of the events in
the image plane and embed the camera trajectory in the covariance kernel
function. In doing so, estimating the trajectory is done similarly to GP
hyperparameter learning by maximising the log marginal likelihood of the data.
The continuous occupancy fields are turned into distance fields and used as
templates for homography-based registration. By benchmarking the proposed
method against other state-of-the-art techniques, we show that our open-source
implementation performs high-accuracy motion compensation and produces
high-quality tracks in real-world scenarios.
