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Appl. Sci. 2019, 9(8), 1559;

Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction

Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Author to whom correspondence should be addressed.
Received: 18 February 2019 / Revised: 31 March 2019 / Accepted: 9 April 2019 / Published: 15 April 2019
(This article belongs to the Section Computing and Artificial Intelligence)
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As a fundamental and challenging problem, non-rigid structure-from-motion (NRSfM) has attracted a large amount of research interest. It is worth mentioning that NRSfM has been applied to dynamic scene understanding and motion segmentation. Especially, a motion segmentation approach combining NRSfM with the subspace representation has been proposed. However, the current subspace representation for non-rigid motions clustering do not take into account the inherent sequential property, which has been proved vital for sequential data clustering. Hence this paper proposes a novel framework to segment the complex and non-rigid motion via an ordered subspace representation method for the reconstructed 3D data, where the sequential property is properly formulated in the procedure of learning the affinity matrix for clustering with simultaneously recovering the 3D non-rigid motion by a monocular camera with 2D point tracks. Experiment results on three public sequential action datasets, BU-4DFE, MSR and UMPM, verify the benefits of method presented in this paper for classical complex non-rigid motion analysis and outperform state-of-the-art methods with lowest subspace clustering error (SCE) rates and highest normalized mutual information (NMI) in subspace clustering and motion segmentation fields. View Full-Text
Keywords: low rank representation; subspace clustering; non-rigid structure-from-motion low rank representation; subspace clustering; non-rigid structure-from-motion

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Du, W.; Li, J.; Wu, F.; Sun, Y.; Hu, Y. Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction. Appl. Sci. 2019, 9, 1559.

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