  In this paper we present an event aggregation strategy to convert the output
of an event camera into frames processable by traditional Computer Vision
algorithms. The proposed method first generates sequences of intermediate
binary representations, which are then losslessly transformed into a compact
format by simply applying a binary-to-decimal conversion. This strategy allows
us to encode temporal information directly into pixel values, which are then
interpreted by deep learning models. We apply our strategy, called Temporal
Binary Representation, to the task of Gesture Recognition, obtaining state of
the art results on the popular DVS128 Gesture Dataset. To underline the
effectiveness of the proposed method compared to existing ones, we also collect
an extension of the dataset under more challenging conditions on which to
perform experiments.
