Motion Segmentation Using Global and Local Sparse Subspace Optimization

In this paper, we propose a new framework for segmenting feature-based moving objects under the affine subspace model. Since the feature trajectories are high-dimensional and contain the noise, we first apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Then, the local subspace separation is obtained using automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation and deal with the missing data problem, we propose an error estimation function to encourage the projected data that span a same local subspace to be clustered together. Finally, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and the sparse neighbor optimization. We evaluate our proposed framework by comparing it to other motion segmentation algorithms. Our method achieves improved performance on state-of-the-art benchmark datasets.