Event cameras are bio-inspired vision sensors which mimic retinas to asynchronously report per-pixel intensity change rather than outputting an actual intensity image at regular interval. 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 architecture, such as CNN, requires dense synchronous input data, thereby cannot make use of the sparseness of the data. Here, we propose EventNet, to the best of our knowledge, the first trainable neural network architecture, which can directly process asynchronous sparse event signals recursively in an event-wise manner. EventNet models dependence of the output on tens of thousands of causal event recursively by the novel temporal coding scheme. As a result, at inference time, our network operates in the event-wise manner which is realized by very few sum-of-the-product operations---table look-up and temporal feature aggregation---which enabled processing of $1$ mega or more event per second on standard CPU. In experiments using real data, we demonstrate 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。

EventNet：异步递归事件处理