Event cameras are bio-inspired vision sensors that mimic retinas to
asynchronously report per-pixel intensity changes rather than outputting an
actual intensity image at regular intervals. This new paradigm of image sensor
offers significant potential advantages; namely, sparse and non-redundant data
representation. Unfortunately, however, most of the existing artificial neural
network architectures, such as a CNN, require dense synchronous input data, and
therefore, cannot make use of the sparseness of the data. We propose EventNet,
a neural network designed for real-time processing of asynchronous event
streams in a recursive and event-wise manner. EventNet models dependence of the
output on tens of thousands of causal events recursively using a novel temporal
coding scheme. As a result, at inference time, our network operates in an
event-wise manner that is realized with very few sum-of-the-product
operations---look-up table and temporal feature aggregation---which enables
processing of 1 mega or more events per second on standard CPU. In experiments
using real data, we demonstrated the real-time performance and robustness of
our framework.

本研究提出了 EventNet，一种基于新型视觉传感器的神经网络，旨在实时处理异步事件流，并采用了新的时间编码方案来建模输出对数万个因果事件的依赖关系，同时在标准 CPU 上每秒处理百万个事件，通过实验验证了该框架的实时性和稳健性。