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
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2019
Volume85Issue:1Pages:50-59
Abstract

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.

KeywordPseudo low rank Data driven Low resolution Action recognition
DOI10.1016/j.patcog.2018.07.033
WOS KeywordACTION RECOGNITION ; DECOMPOSITION
Indexed BySCI
Language英语
Funding ProjectNational 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]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000447819300005
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22783
Collection先进数据分析与学习团队
Corresponding AuthorYu, Tingzhao
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yu, Tingzhao,Wang, Lingfeng,Guo, Chaoxu,et al. Pseudo low rank video representation[J]. PATTERN RECOGNITION,2019,85(1):50-59.
APA Yu, Tingzhao,Wang, Lingfeng,Guo, Chaoxu,Gu, Huxiang,Xiang, Shiming,&Pan, Chunhong.(2019).Pseudo low rank video representation.PATTERN RECOGNITION,85(1),50-59.
MLA Yu, Tingzhao,et al."Pseudo low rank video representation".PATTERN RECOGNITION 85.1(2019):50-59.
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