摘要
基于图像的运动估计是计算机视觉在许多应用中的一项基本任务,其主要目标是尽可能精确地估计场景和物体的运动.文中针对界外值在光流估计过程中会引起不可预期的运动估计结果、严重影响运动估计精度的问题,提出一种改进的鲁棒分层的多尺度运动估计算法.该算法利用图基的双权重函数,自动调节不同残差数据点的权重,去除残差过大的数据点,并采用多尺度金字塔由粗到精逐层迭代,精确地估计运动矢量.实验结果表明:该算法鲁棒性好,能有效地解决遮挡背景和运动不连续而引起的界外值问题,明显地提高运动估计精度.
The motion estimation based on image is a basic task in many applications of computer vision. The main goal of it is to estimate the motion of scenes and objects as accurate as possible. In the optical flow estimation, outliers may lead to undesirable results and greatly reduce the motion estimation precision. In order to solve these problems, an improved robust multi-scale algorithm of hierarchical motion estimation is proposed. In this algorithm, Tukey＇s bi-weight function is used to automatically adjust the weights of data with different residual errors and to remove the data with excessive residual errors, and a multi-scale pyramid is employed to accurately estimate the motion vector by an iteration gradually from coarseness to fine. Experimental results show that the proposed algorithm with strong robustness effectively solves the problem of outliers caused by occlusive background and discontinuous motion and greatly improves the precision of motion estimation.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第11期118-122,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(50775073)
广东省教育部产学研结合项目(2006D930304001)
广州市科技攻关项目(2006A10401004)
关键词
运动估计
光流
多尺度
鲁棒性
motion estimation
optical flow
multiple scale
robustness
