HRL Laboratories, LLC, 3011 Malibu Canyon Rd., Malibu, CA 90265, USA.
IEEE Trans Pattern Anal Mach Intell. 2010 Oct;32(10):1832-45. doi: 10.1109/TPAMI.2009.191.
In this paper, we study the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera model, this problem can be cast as the problem of segmenting samples drawn from multiple linear subspaces. In practice, due to limitations of the tracker, occlusions, and the presence of nonrigid objects in the scene, the obtained motion trajectories may contain grossly mistracked features, missing entries, or corrupted entries. In this paper, we develop a robust subspace separation scheme that deals with these practical issues in a unified mathematical framework. Our methods draw strong connections between lossy compression, rank minimization, and sparse representation. We test our methods extensively on the Hopkins155 motion segmentation database and other motion sequences with outliers and missing data. We compare the performance of our methods to state-of-the-art motion segmentation methods based on expectation-maximization and spectral clustering. For data without outliers or missing information, the results of our methods are on par with the state-of-the-art results and, in many cases, exceed them. In addition, our methods give surprisingly good performance in the presence of the three types of pathological trajectories mentioned above. All code and results are publicly available at http://perception.csl.uiuc.edu/coding/motion/.
在本文中，我们研究了在图像序列中分割跟踪到的多个运动物体特征点轨迹的问题。使用仿射相机模型，这个问题可以转化为从多个线性子空间中抽取样本的问题。在实际应用中，由于跟踪器的限制、遮挡和场景中存在非刚体物体，得到的运动轨迹可能包含严重误跟踪的特征、缺失的条目或损坏的条目。在本文中，我们开发了一种鲁棒的子空间分离方案，该方案在统一的数学框架内处理这些实际问题。我们的方法在有损压缩、秩最小化和稀疏表示之间建立了紧密的联系。我们在 Hopkins155 运动分割数据库和其他具有离群值和缺失数据的运动序列上对我们的方法进行了广泛的测试。我们将我们的方法与基于期望最大化和谱聚类的最新运动分割方法进行了比较。对于没有离群值或缺失信息的数据，我们的方法的结果与最先进的结果相当，在许多情况下甚至超过了它们。此外，我们的方法在存在上述三种类型的病态轨迹时表现出惊人的良好性能。所有的代码和结果都可以在 http://perception.csl.uiuc.edu/coding/motion/ 上公开获取。
