Optical Flow for Rigid Multi-Motion Scenes

We observe that in many applications, the motion present in a scene is well characterized by a small number of (rigid) motion hypotheses. Based on this observation, we present rigid multi-motion optical flow (RMM). By restricting flow to one of several motion hypotheses, RMM produces more accurate optical flow than arbitrary motion models. We evaluate an algorithm based on RMM on a novel synthetic dataset, consisting of 12 photo-realistically rendered scenes containing rigid vehicular motion and a corresponding, exact, ground truth. On this dataset, we demonstrate a substantial advantage of RMM over general-purpose algorithms: going from 36% outliers with the DiscreteFlow algorithm, to 26% with ours, with a mean error reduction from 8.4px to 6.9px. We also perform qualitative evaluation on real-world imagery from traffic cameras.

我们观察到，在许多应用中，场景中存在的运动的特征是少数（刚性）运动假设。基于此观察，我们提出了刚性多动光流（RMM）。通过将流量限制在几个运动假设之一中，RMM比任意运动模型产生更准确的光流。我们在新的合成数据集上评估了基于RMM的算法，该数据集由12个真实的渲染场景组成，其中包含刚性车辆运动和相应的，精确的，地面真相。在此数据集上，我们证明了RMM比通用算法的实质优势：从具有离散流算法的36％离群值的转变为26％，平均误差从8.4px降低到6.9px。我们还对交通摄像机的现实世界图像进行定性评估。