Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising
	Feng, Yinfu 1; Ji, Mingming 1; Xiao, Jun 1; Yang, Xiaosong 2; Zhang, Jian J.2; Zhuang, Yueting 1; Li, Xuelong3
刊名	ieee transactions on cybernetics
	2015-12-01
卷号	45 期号:12 页码:2693-2706
关键词	Human motion denoising l(2 Microsoft Kinect motion capture data robust dictionary learning robust structured sparse coding p)-norm
英文摘要	motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, film production, and medical rehabilitation. even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. to denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high complexity of human motion and the diversity of real-life situations. in this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal patterns and the structural sparsity embedded in motion data. we first replace the regularly used entire pose model with a much fine-grained partlet model as feature representation to exploit the abundant local body part posture and movement similarities. then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution information and the temporal smoothness property of human motion have been jointly taken into account. compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. the outputs of our approach are much more stable than that of the others. in addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, cybernetics
研究领域[WOS]	computer science
关键词[WOS]	action recognition ; capture ; representation ; images ; animation ; wavelets ; pursuit ; noise
收录类别	SCI
语种	英语
WOS记录号	WOS:000365320300006
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/27547]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China 2.Bournemouth Univ, Natl Ctr Comp Animat, Poole BH12 5BB, Dorset, England 3.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Feng, Yinfu,Ji, Mingming,Xiao, Jun,et al. Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising[J]. ieee transactions on cybernetics,2015,45(12):2693-2706.
APA	Feng, Yinfu.,Ji, Mingming.,Xiao, Jun.,Yang, Xiaosong.,Zhang, Jian J..,...&Li, Xuelong.(2015).Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising.ieee transactions on cybernetics,45(12),2693-2706.
MLA	Feng, Yinfu,et al."Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising".ieee transactions on cybernetics 45.12(2015):2693-2706.