基于运动优化语义分割的变分光流计算方法

Variational Optical Flow Computation Method Based on Motion Optimization Semantic Segmentation

针对光照变化和大位移运动等复杂场景下图像序列变分光流计算的边缘模糊与过度分割问题,文中提出基于运动优化语义分割的变分光流计算方法.首先,根据图像局部区域的去均值归一化匹配模型,构建变分光流计算能量泛函.然后,利用去均值归一化互相关光流估计结果,获取图像运动边界信息,优化语义分割,设计运动约束语义分割的变分光流计算模型.最后,融合图像不同标签区域光流,获得光流计算结果.在Middlebury、UCF101数据库上的实验表明,文中方法的光流估计精度与鲁棒性较高,尤其对光照变化、弱纹理和大位移运动等复杂场景的边缘保护效果较优.

To address the issues of edge-blurring and over-segmentation of image sequence optical flow computation under complex scenes,such as illumination change and large displacement motions,a variational optical flow computation method based on motion optimization semantic segmentation is proposed.Firstly,an energy function of variational optical flow computation is constructed via a image local region based zero-mean normalized cross correlation matching model.Then,the motion boundary information obtained from the computed optical flows is utilized to optimize the initial image semantic segmentation result,and a variational optical flow computation model based on the motion constraint semantic segmentation is constructed.Next,the optical flows of various label areas are fused to acquirethe refined flow field.Finally,experimental results on Middlebury and UCF101 databases demonstrate that the proposed method performs well in computation accuracy and robustness,especially for the edge-preserving with illumination change,textureless regions and large displacement motions.