Human Motion Segmentation via Robust Kernel Sparse Subspace Clustering

• Published in: IEEE transactions on image processing Vol. 27; no. 1; pp. 135 - 150
• Main Authors: Xia, Guiyu, Sun, Huaijiang, Feng, Lei, Zhang, Guoqing, Liu, Yazhou
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2018
• Subjects: Computer vision; correntropy; Data models; Kernel; Manifolds; Motion capture data; Motion segmentation; Riemannian manifold; robust segmentation; Robustness; subspace clustering; Manifolds; Computer vision; Data models; Robustness; Motion segmentation; Kernel
• ISSN: 1057-7149; 1941-0042
• 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.