3D human pose regression via robust sparse tensor subspace learning
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- 作者：Jialin Yu ; Jifeng Sun
- 关键词：Human pose estimation ; Tensor learning ; Sparse representation ; Support tensor regression ; Sparse projection transformation ; Feature extraction
- 刊名：Multimedia Tools and Applications
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：76
- 期：2
- 页码：2399-2439
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
- 出版者：Springer US
- ISSN：1573-7721
- 卷排序：76
文摘
In this paper, we present a novel algorithm called robust sparse tensor subspace learning (RSTSL) for 3D human pose regression, and it further extends the latest presented tensor learning to a sparse case. A set of interrelated sparse discriminant projection matrices for feature extraction can be obtained by introducing k-mode optimization and elastic-net algorithm into the objective function of RSTSL and each non-zero element in each discriminant projection matrix is selected from the most typical variables. Thus, the most important low-dimensional tensor feature (LDTF) that corresponds to a test image (i.e., high-order tensor) is extracted through sparse projection transformation. Moreover, we present a novel regression model called optimal-order support tensor regression (OOSTR) to build a finest mapping function between LDTF and 3D human pose configuration. Extensive simulations are conducted on two human motion databases, HumanEva and Brown databases, experimental results show that our proposed RSTSL can not only weaken the sensitivity to incoherent human motions caused by transient occlusion of cameras, sudden change in human velocity and low-frame rate but also strengthen the robustness to silhouette ambiguity, obstacle occlusion and random noise. All the results have confirmed that our tracking system achieves the most significant performance against the compared the state-of-the-art approaches, especially in the complicated human motion databases with different clustered backgrounds, human movements, clothing-style, illumination and subjects like HumanEva database.