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 …