Optical flow estimation method under the condition of illumination change

Liu Jun; Zu Jing; Zhang Yu; Zhang Hongyan

doi:10.11834/jig.20141009

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Optical flow estimation method under the condition of illumination change
Vol. 19, Issue 10, Pages: 1475-1480(2014)
Published Online：13 October 2014，

Published：2014
DOI： 10.11834/jig.20141009
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DOI：

Liu Jun, Zu Jing, Zhang Yu, Zhang Hongyan. Optical flow estimation method under the condition of illumination change[J]. Journal of Image and Graphics, 2014, 19(10): 1475-1480. DOI： 10.11834/jig.20141009.

摘要

为了提高光流法在处理光照变化和大位移方面的稳健性。提出一种结合结构纹理分解预处理和加权中值滤波的光流场模型。该方法数据项采用灰度守恒假设和梯度守恒假设相结合、局部约束与全局约束相结合的思想。同时采用结构纹理分解、加权中值滤波、金字塔结构等高效的光流估计技术，进一步增强了光流算法的精确性与实用性。分别通过Middlebury光流数据库图像和真实场景图像对提出的光流估计算法进行了大量实验验证。实验结果表明，改进的光流估计法在处理光照变化方面表现不错，不仅获得稠密的光流场，而且提高了光流场准确提取目标边缘的能力。和传统光流方法相比，所提方法在光照变化情况下能获得更加理想的结果，降低了实际场景中光线变化的干扰，能更好地适用于实际场景中。

Abstract

In order to improve robustness of the optical flow method in the treatment of the illumination change and large displacement. The optical flow estimation method combining structure texture decomposition preprocessing and weighted median filter is proposed. The idea to obtain the data term is to use the combination of gray and gradient constancy assumption

the combination of local and global constraint. Meanwhile

the use of texture decomposition

the weighted median filter and the pyramid structure is further enhance the accuracy and practicability of the optical flow algorithm. The proposed method is evaluated by using both the Middlebury optical flow database images and real scene images. The experimental results show improved optical flow estimation method in the treatment of the illumination change performance is good

not only to obtain dense optical flow field

and improves the detection precision optical flow field on the edge of the target. Compared with traditional optical flow method

the proposed method under the condition of illumination change can obtain a more ideal result and reduce the interference of the actual light changes in a scene. The optical flow method can better apply to actual scenario.