A hierarchical optical flow estimation algorithm based on the interlevel motion smoothness constraint
A hierarchical optical flow estimation algorithm based on the interlevel motion smoothness constraint
复制标题
基于层间运动平滑约束的分层光流估计算法
DOI：
10.1016/0031-3203(93)90059-6
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发表时间：
1993
期刊：
影响因子：
--
通讯作者：
Sang Uk Lee
中科院分区：
文献类型：
--
作者：
S. Hwang;Sang Uk Lee
提出了一种基于图像多分辨率表示的光流估计算法，即图像金字塔算法。由于图像金字塔由图像序列的几个低通滤波版本组成，因此每个金字塔级别的运动向量也是最精细级别的运动向量的低通滤波版本。因此，在运动估计中引入层间运动平滑约束，通过定义算子在相邻的两个金字塔层次之间建立适当的关系。图像金字塔的使用使我们能够估计较大的流动向量，并容易地采用多重网格技术来提高算法的收敛速度。仿真结果表明，与Horn和Schunck算法等基于光滑度约束的运动估计方法相比，该算法具有更高的运动估计精度。此外，本文还对该算法的收敛行为进行了理论分析。
An optical flow estimation algorithm is proposed based on the multiresolution representation of the image, i.e. the image pyramid. Since the image pyramid consists of several low-pass filtered versions of the image sequence, the motion vector at each pyramid level is also the low-pass filtered versions of the motion vector at the finest level. Thus, the interlevel motion smoothness constraint is introduced in the motion estimation by defining the operators to establish an appropriate relationship between two adjacent pyramid levels. The employment of the image pyramid allows us to estimate the large flow vectors and to adopt easily the multigrid technique to improve the convergence speed of the proposed algorithm. The simulation results reveal that the proposed algorithm provides more accurate motion estimation, compared to the methods using the smoothness constraints such as Horn and Schunck's algorithm. In addition, the theoretical analysis of the convergence behavior of the proposed algorithm is also provided in this paper.