  Recently, Dynamic Vision Sensors (DVSs) sparked a lot of interest due to
their inherent advantages over conventional RGB cameras. These advantages
include a low latency, a high dynamic range and a low energy consumption.
Nevertheless, the processing of DVS data using Deep Learning (DL) methods
remains a challenge, particularly since the availability of event training data
is still limited. This leads to a need for event data augmentation techniques
in order to improve accuracy as well as to avoid over-fitting on the training
data. Another challenge especially in real world automotive applications is
occlusion, meaning one object is hindering the view onto the object behind it.
In this paper, we present a novel event data augmentation approach, which
addresses this problem by introducing synthetic events for randomly moving
objects in a scene. We test our method on multiple DVS classification datasets,
resulting in an relative improvement of up to 6.5 % in top1-accuracy. Moreover,
we apply our augmentation technique on the real world Gen1 Automotive Event
Dataset for object detection, where we especially improve the detection of
pedestrians by up to 5 %.
