Event-Based Velocity Prediction for Spiking Neural Networks

This dataset is intended to be used to predict the velocity based on the event pixels present in the data, given in (t, x, y, p) format alongside a ground truth velocity reading. A novel dataset using people and various objects moving in front of an RGB video camera was created. The positions of each entity and the associated times were captured with a Vicon motion tracking system. These two types of data were calibrated so that the movement in the video matched the measurements recorded by the Vicon system. The types of data collected include two different people carrying a calibrated Vicon Active Wand and moving around the room, a Lambda aerial robot with motion tracking markers that flew around the room, a box with motion tracking markers that were tossed back and forth in the air, and the same box was slid across the floor. The video recording was simulated as event camera data. Each pixel changes state independently of all the other pixels. The Open Event Camera Simulator (ESIM) from the Robotics and Perception Group at the University of Zurich and ETH Zurich is used. The event camera simulator used, ESIM, allows for accurate event simulation data. The ground truth velocities were calculated from the position and timestamps recorded by the Vicon system.

本数据集旨在根据数据中以(t, x, y, p)格式提供的事件像素预测运动速度，同时附带真实速度真值标注。我们构建了一个新颖的数据集，采集了人群及各类物体在RGB摄像机前运动的场景。各实体的位置与对应时刻通过Vicon运动捕捉系统（Vicon motion tracking system）采集获取，随后对两类数据进行校准，确保视频中的运动与Vicon系统记录的测量结果完全匹配。本次采集的数据涵盖以下四类场景：两名携带经校准的Vicon有源棒（Vicon Active Wand）的人员在室内往复走动；一台搭载运动捕捉标记点的Lambda空中机器人（Lambda aerial robot）在室内飞行；一个贴有运动捕捉标记点的箱子被反复抛接至空中；以及该箱子在地面上滑行。本次实验将录制的视频转换为事件相机数据，事件相机的每个像素均可独立于其余像素改变自身状态。我们采用了苏黎世大学与苏黎世联邦理工学院机器人与感知组开发的开源事件相机模拟器（Open Event Camera Simulator, ESIM），该模拟器可生成高精度的事件模拟数据。真实速度真值由Vicon系统记录的位置与时间戳计算得到。