ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 475-482, 2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
20 Aug 2015
M. Ying Yang1, S. Feng2, H. Ackermann2, and B. Rosenhahn2 1Computer Vision Lab, TU Dresden, Dresden, Germany
2Institute for Information Processing (TNT), Leibniz University Hannover, Hannover, Germany
Keywords: Motion segmentation, Affine subspace model, Sparse PCA, Subspace estimation, Optimization Abstract. 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, 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. 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.
Conference paper (PDF, 8241 KB)

Citation: Ying Yang, M., Feng, S., Ackermann, H., and Rosenhahn, B.: GLOBAL AND LOCAL SPARSE SUBSPACE OPTIMIZATION FOR MOTION SEGMENTATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 475-482,, 2015.

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