We examine the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera model, this motion segmentation problem can be cast as the problem of segmenting samples drawn from a union of linear subspaces. 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 not correspond to any valid motion model. In this paper, we develop a robust subspace separation scheme that can deal with all of these practical issues in a unified framework. Our methods draw strong connections between lossy compression, rank minimization, and sparse representation. We test our methods extensively and compare their performance to several extant methods with experiments on the Hopkins 155 database. Our results are on par with stateof-the-art results, and in many cases exceed them. All MAT-LAB code and segmentation results are publicly available for peer evaluation at http://perception.csl.uiuc. edu/coding/motion/.
For example in REF , a robust subspace separation method has been developed that uses Agglomerative Lossy Compression (ALC) for motion segmentation.
Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories
2008 IEEE Conference on Computer Vision and Pattern Recognition
2008 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science