  Event cameras are ideal for object tracking applications due to their ability
to capture fast-moving objects while mitigating latency and data redundancy.
Existing event-based clustering and feature tracking approaches for
surveillance and object detection work well in the majority of cases, but fall
short in a maritime environment. Our application of maritime vessel detection
and tracking requires a process that can identify features and output a
confidence score representing the likelihood that the feature was produced by a
vessel, which may trigger a subsequent alert or activate a classification
system. However, the maritime environment presents unique challenges such as
the tendency of waves to produce the majority of events, demanding the majority
of computational processing and producing false positive detections. By
filtering redundant events and analyzing the movement of each event cluster, we
can identify and track vessels while ignoring shorter lived and erratic
features such as those produced by waves.
