  Visual object tracking under challenging conditions of motion and light can
be hindered by the capabilities of conventional cameras, prone to producing
images with motion blur. Event cameras are novel sensors suited to robustly
perform vision tasks under these conditions. However, due to the nature of
their output, applying them to object detection and tracking is non-trivial. In
this work, we propose a framework to take advantage of both event cameras and
off-the-shelf deep learning for object tracking. We show that reconstructing
event data into intensity frames improves the tracking performance in
conditions under which conventional cameras fail to provide acceptable results.
