基于事件相机的连续光流估计

事件相机对场景的亮度变化进行成像, 输出异步事件流, 具有极低的延时, 受运动模糊问题影响较少. 因此, 可以利用事件相机解决高速运动场景下的光流(Optical flow, OF)估计问题. 基于亮度恒定假设和事件产生模型, 利用事件相机输出事件流的低延时性质, 融合存在运动模糊的亮度图像帧, 提出基于事件相机的连续光流估计算法, 提升了高速运动场景下的光流估计精度. 实验结果表明, 相比于现有的基于事件相机的光流估计算法, 该算法在平均端点误差、平均角度误差和均方误差3个指标上, 分别提升11%、45% 和8%. 在高速运动场景下, 该算法能够准确重建出高速运动目标的连续光流, 保证了存在运动模糊情况时, 光流估计的精度.

Event-based Continuous Optical Flow Estimation

Event camera encodes the brightness change of the scene and outputs asynchronous event data, with extremely low delay and few motion blur problem. Therefore, event camera can be used to solve the problem of optical flow (OF) estimation in high-speed motion scenes. In this paper, based on the assumption of constant brightness and event generation model, continuous optical flow estimation algorithm based on event camera is proposed by utilizing the low-delay property of event stream and fusing the brightness images with motion blur, which greatly improves the optical flow estimation accuracy in high-speed motion scenes. Experimental results show that the proposed algorithm improves the average endpoint error, the average angular error and the mean square error by 11%, 45% and 8%, respectively, comparing with the existing event-based optical flow estimation algorithms. In high-speed motion scenes, the proposed algorithm can accurately reconstruct the continuous optical flow of the high-speed moving target, thus guaranteeing the accuracy of optical flow estimation in the presence of motion blur.