FOE-based Regularization for Optical Flow Estimation from an In-vehicle Event Camera

Abstract

Optical flow estimation in onboard cameras is an important task in automatic driving and advanced driver- assistance systems. However, there is a problem that calculation is mistakable with high contrast and high speed. Event cameras have great features such as high dynamic range and low latency that can overcome these problems. Event cameras report only the change in the logarithmic intensity per pixel rather than the absolute brightness. There is a method of estimating the optical ow simultaneously with the luminance restoration from the event data. The regularization using the L1 norm of differentiation is insufficient for spatially sparse event data. Therefore, we propose to use the focus of expansion (FOE) for regularization of optical ow estimation in event camera. The FOE is defined as the intersection of the translation vector of the camera and the image plane. The optical ow becomes radial from the FOE excluding the rotational component. Using the property, the optical ow can be regularized in the correct direction in the optimization process. We demonstrated that the optical ow was improved by introducing our regularization using the public dataset.