Motion Detail Preserving Optical Flow Estimation

被引:40
|
作者
Xu, Li [1 ]
Jia, Jiaya [1 ]
Matsushita, Yasuyuki [2 ]
机构
[1] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
[2] Microsoft Res Asia, Beijing 100080, Peoples R China
关键词
D O I
10.1109/CVPR.2010.5539820
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We discuss the cause of a severe optical flow estimation problem that fine motion structures cannot always be correctly reconstructed in the commonly employed multiscale variational framework. Our major finding is that significant and abrupt displacement transition wrecks small-scale motion structures in the coarse-to-fine refinement. A novel optical flow estimation method is proposed in this paper to address this issue, which reduces the reliance of the flow estimates on their initial values propagated from the coarser level and enables recovering many motion details in each scale. The contribution of this paper also includes adaption of the objective function and development of a new optimization procedure. The effectiveness of our method is borne out by experiments for both large-and small-displacement optical flow estimation.
引用
收藏
页码:1293 / 1300
页数:8
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