  Event-based cameras, also known as neuromorphic cameras, are bioinspired
sensors able to perceive changes in the scene at high frequency with low power
consumption. Becoming available only very recently, a limited amount of work
addresses object detection on these devices. In this paper we propose two
neural networks architectures for object detection: YOLE, which integrates the
events into surfaces and uses a frame-based model to process them, and fcYOLE,
an asynchronous event-based fully convolutional network which uses a novel and
general formalization of the convolutional and max pooling layers to exploit
the sparsity of camera events. We evaluate the algorithm with different
extensions of publicly available datasets and on a novel synthetic dataset.
