CASIA OpenIR  > 先进数据分析与学习团队
Pseudo low rank video representation
Yu, Tingzhao1,2; Wang, Lingfeng1; Guo, Chaoxu1,2; Gu, Huxiang1; Xiang, Shiming1; Pan, Chunhong1
发表期刊PATTERN RECOGNITION
ISSN0031-3203
2019
卷号85页码:50-59
通讯作者Yu, Tingzhao(tingzhao.yu@nlpr.ia.ac.cn)
摘要Action recognition plays a fundamental role in computer vision and has drawn growing attention recently. This paper addresses this issue conditioned on extreme Low Resolution (abbreviated as eLR). Generally, eLR video is often susceptible to noise, thus extracting a robust representation is of great challenge. Besides, due to the limitation of video resolution, eLR video cannot be cropped or resized randomly, then it is inevitably complicated to design and to train a deep network for eLR video. This paper proposes a novel network for robust video representation by employing pseudo tensor low rank regularization. A new Video Low Rank Representation model (named VLRR) is first proposed to recover the inherent robust component of a given video, and then the recovered term is introduced to a convolutional Network (denoted pLRN) as an auxiliary pseudo Low Rank guidance. Benefitting from the auxiliary guidance, pLRN can learn an approximate low rank term end-to-end. Besides, this paper presents a new initialization strategy for eLR recognition neTwork based on Tensor factorization (dubbed TenneT). TenneT is data-driven and learns the convolutional kernels totally from the video distribution while without any back-propagation. It outperforms random initialization both in speed and accuracy. Experiments on benchmark datasets demonstrate the effectiveness and superiority of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.
关键词Pseudo low rank Data driven Low resolution Action recognition
DOI10.1016/j.patcog.2018.07.033
关键词[WOS]ACTION RECOGNITION ; DECOMPOSITION
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61620106003]
项目资助者National Natural Science Foundation of China
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000447819300005
出版者ELSEVIER SCI LTD
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/22783
专题先进数据分析与学习团队
通讯作者Yu, Tingzhao
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
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GB/T 7714
Yu, Tingzhao,Wang, Lingfeng,Guo, Chaoxu,et al. Pseudo low rank video representation[J]. PATTERN RECOGNITION,2019,85:50-59.
APA Yu, Tingzhao,Wang, Lingfeng,Guo, Chaoxu,Gu, Huxiang,Xiang, Shiming,&Pan, Chunhong.(2019).Pseudo low rank video representation.PATTERN RECOGNITION,85,50-59.
MLA Yu, Tingzhao,et al."Pseudo low rank video representation".PATTERN RECOGNITION 85(2019):50-59.
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