  Event-based cameras have become increasingly popular for tracking fast-moving
objects due to their high temporal resolution, low latency, and high dynamic
range. In this paper, we propose a novel algorithm for tracking event blobs
using raw events asynchronously in real time. We introduce the concept of an
event blob as a spatio-temporal likelihood of event occurrence where the
conditional spatial likelihood is blob-like. Many real-world objects generate
event blob data, for example, flickering LEDs such as car headlights or any
small foreground object moving against a static or slowly varying background.
The proposed algorithm uses a nearest neighbour classifier with a dynamic
threshold criteria for data association coupled with a Kalman filter to track
the event blob state. Our algorithm achieves highly accurate tracking and event
blob shape estimation even under challenging lighting conditions and high-speed
motions. The microsecond time resolution achieved means that the filter output
can be used to derive secondary information such as time-to-contact or range
estimation, that will enable applications to real-world problems such as
collision avoidance in autonomous driving.
