许多特征跟踪算法已被提出

为运动分割,但由此而轨迹

不一定是正确的。在本文中,

我们提出一种技术用于去除野值的基础上

在对知识,正确的轨迹约束的

在他们的网域的子空间的。我们第一次

合适的子空间的轨迹鲁检测

然后用RANSAC移除那些大型

后遗症。使用真实的视频序列,我们证明了

我们的方法是有效的,即使多个对象

移动在场景里。我们也证实分离

我们确实是提高精度的方法。

-Many feature tracking algorithms have been proposed

for motion segmentation, but the resulting trajectories

are not necessarily correct. In this paper,

we propose a technique for removing outliers based

on the knowledge that correct trajectories are constrained

to be in a subspace of their domain. We first

fit the subspace to the detected trajectories robustly

using RANSAC and then remove those that have large

residuals. Using real video sequences, we demonstrate

that our method is effective even if multiple objects are

moving in the scene. We also confirm that the separation

accuracy is indeed improved by our method.