In this paper, we propose a new framework for segmenting feature-based moving
objects under affine subspace model. Since the feature trajectories in practice
are high-dimensional and contain a lot of noise, we firstly apply the sparse
PCA to represent the original trajectories with a low-dimensional global
subspace, which consists of the orthogonal sparse principal vectors.
Subsequently, the local subspace separation will be achieved via automatically
searching the sparse representation of the nearest neighbors for each projected
data. In order to refine the local subspace estimation result and deal with the
missing data problem, we propose an error estimation to encourage the projected
data that span a same local subspace to be clustered together. In the end, the
segmentation of different motions is achieved through the spectral clustering
on an affinity matrix, which is constructed with both the error estimation and
sparse neighbors optimization. We test our method extensively and compare it
with state-of-the-art methods on the Hopkins 155 dataset and Freiburg-Berkeley
Motion Segmentation dataset. The results show that our method is comparable
with the other motion segmentation methods, and in many cases exceed them in
terms of precision and computation time.

本文提出了一种基于仿射子空间模型的特征运动物体分割新框架，该方法通过稀疏 PCA 表示原始轨迹，通过自动搜索最近邻点的稀疏表达来实现局部子空间分离，并提出了一种误差估计来处理缺失数据问题，最后通过谱聚类方法将不同的运动分割出来，实验证明该方法在精度和运算时间上优于其他运动分割方法。