FOE-based regularization for optical flow estimation from an in-vehicle event camera

摘要

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.