School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
IEEE Trans Image Process. 2018;27(1):135-150. doi: 10.1109/TIP.2017.2738562.
Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.
人体运动研究受到了广泛关注。与视频相比，人体运动捕捉数据更精确地记录了人体运动，已被广泛应用于许多领域。运动分割是许多相关应用中不可或缺的一步，但目前的运动捕捉数据分割方法不能有效地模拟运动捕捉数据的一些重要特征，如黎曼流形结构和包含非高斯噪声。在本文中，我们将运动捕捉数据的分割转化为一个时间子空间聚类问题。在稀疏子空间聚类的框架下，我们提出使用测地指数核来建模黎曼流形结构，使用相关熵来度量重构误差，使用三角形约束来保证每个聚类中的时间连续性，并使用多视图重建来提取不同关节之间的关系。因此，利用运动捕捉数据的一些特殊特征，我们提出了一种新的分割方法，由于相关熵是一种局部相似性度量，因此该方法对非高斯噪声具有鲁棒性。我们还开发了一种基于块坐标下降法的高效优化算法来解决所提出的模型。我们的优化算法具有线性复杂度，而稀疏子空间聚类最初是一个二次问题。在模拟噪声数据集和真实噪声数据集上的广泛实验结果表明了所提出方法的优势。
