Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
复制标题
DOI：
--
复制
发表时间：
--
期刊：
影响因子：
--
通讯作者：
T. Brox;Jitendra Malik
中科院分区：
文献类型：
--
作者：
T. Brox;Jitendra Malik
-光流估计的经典标志是对时间密集采样的要求。虽然从粗略到精细的翘曲方案以某种方式放松了这种限制，但结构的规模和可以估计的速度之间存在内在的依赖关系。这尤其使得对人体详细运动的估计产生了问题，因为身体的小部位可以移动得非常快。在本文中，我们提出了一种方法，通过将丰富的描述符集成到变分光流设置中来解决这个问题。通过这种方法，我们可以以几乎与从变分光流中已知的相同的高精度来估计密集光流场，同时扩展到运动分析的新领域，其中不再满足在时间上进行密集采样的要求。
—Optical flow estimation is classically marked by the requirement of dense sampling in time. While coarse-to-fine warping schemes have somehow relaxed this constraint, there is an inherent dependency between the scale of structures and the velocity that can be estimated. This particularly renders the estimation of detailed human motion problematic, as small body parts can move very fast. In this paper, we present a way to approach this problem by integrating rich descriptors into the variational optical flow setting. This way we can estimate a dense optical flow field with almost the same high accuracy as known from variational optical flow, while reaching out to new domains of motion analysis where the requirement of dense sampling in time is no longer satisfied.