  Event cameras are novel sensors that perceive the per-pixel intensity changes
and output asynchronous event streams, showing lots of advantages over
traditional cameras, such as high dynamic range (HDR) and no motion blur. It
has been shown that events alone can be used for object tracking by motion
compensation or prediction. However, existing methods assume that the target
always moves and is the stand-alone object. Moreover, they fail to track the
stopped non-independent moving objects on fixed scenes. In this paper, we
propose a novel event-based object tracking framework, called SiamEvent, using
Siamese networks via edge-aware similarity learning. Importantly, to find the
part having the most similar edge structure of target, we propose to correlate
the embedded events at two timestamps to compute the target edge similarity.
The Siamese network enables tracking arbitrary target edge by finding the part
with the highest similarity score. This extends the possibility of event-based
object tracking applied not only for the independent stand-alone moving
objects, but also for various settings of the camera and scenes. In addition,
target edge initialization and edge detector are also proposed to prevent
SiamEvent from the drifting problem. Lastly, we built an open dataset including
various synthetic and real scenes to train and evaluate SiamEvent. Extensive
experiments demonstrate that SiamEvent achieves up to 15% tracking performance
enhancement than the baselines on the real-world scenes and more robust
tracking performance in the challenging HDR and motion blur conditions.
