Representing motion information from event-based cameras

Many recent works have successfully leveraged motion information (i.e., dense optical flow) for a variety of problems. In this paper, we introduce a methodology to capture motion information using high-speed event-based cameras combined with convolutional neural networks (CNN). Our motion event features (MEFs) succinctly capture motion magnitude and direction in a form suitable for input into a CNN. We demonstrate the broad applicability of MEFs across two disparate problems: action recognition, and autonomous robot reactive control.