Optical flow estimation from an in‐vehicle camera is an important task in automatic driving and advanced driver‐assistance systems. However, there is a problem that optical flow estimation is mistakable with high contrast and high speed. Event camera can overcome these situations because it reports only the per‐pixel intensity change with high dynamic range and low latency. However, the L1 smoothness regularization in the conventional optical flow estimation method is not suitable for radial optical flow in the driving scene. Therefore, we propose to use the focus of expansion (FOE) for regularization of optical flow estimation in event camera. The FOE is defined as the intersection of the translation vector of the camera and the image plane. The optical flow becomes radial from the FOE excluding the rotational component. Using the property, the optical flow can be regularized in the correct direction in the optimization process. We demonstrated that the optical flow was improved by introducing our regularization using the public dataset.

中文翻译：

---

基于FOE的正则化，用于车载事件摄像机的光流估计

在自动驾驶和高级驾驶员辅助系统中，车载摄像机的光流估计是一项重要任务。然而，存在这样的问题，即，光流估计可能以高对比度和高速度模糊。事件摄像机可以克服这些情况，因为它仅报告每像素强度变化，并具有高动态范围和低延迟。然而，传统的光流估计方法中的L1平滑度正则化不适用于驾驶场景中的径向光流。因此，我们建议将扩展焦点（FOE）用于事件相机中光流估计的正则化。FOE定义为相机的平移矢量和像平面的交点。除了旋转分量外，光流从FOE开始呈放射状。使用该属性，在优化过程中，光流可以在正确的方向上调整。我们证明通过使用公共数据集引入正则化可以改善光流。