基于分层置信度传播的光流估计方法

Hierarchical Belief Propagation for Optical Flow Estimation

置信度传播算法作为一种有效的寻找图像间对应点的方法,近年来被广泛应用于光流估计.但是在估计大位移高精度光流时,将置信度传播直接应用于原图像会导致标签空间过大和处理时间过长的问题.为了克服这个缺点,我们提出了一种基于分层置信度传播的算法来估计高精度大位移光流.本文方法将输入图像视作马尔科夫随机场,为了提高效率,在超像素和像素两个层面上执行置信度传播.我们将超像素层得到的基础位移结果作为粗略的位移参考值,可以有效地减小像素层置信度传播的标签空间,并在有限的标签空间内得到高精度的光流估计结果.MPI Sintel光流数据集上的实验结果显示本文提出的方法在精度和速度上都取得了较好的结果.

As an effective way to find correspondences between images, Belief Propagation（BP） is widely used for estimating optical flow in recent years. Nevertheless, its application to directly estimating high-accuracy large displacement optical flow needs huge label space and long time to process. In order to overcome the drawback of BP, we propose a Hierarchical Belief Propagation（HBP） algorithm to estimate high-accuracy large displacement optical flow. We treat input images as Markov Random Fields（MRFs）. To accelerate computation, we perform BP on hierarchical MRFs,i.e., superpixel MRF and pixel MRF. The basic displacements obtained on the superpixel MRF are used as a coarse reference to constrain label space to a smaller size on the pixel MRF. Based on this constrained label space, we can estimate accurate optical flow efficiently. Experiments on MPI Sintel dataset show that the proposed method is competitive on speed and accuracy.