中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
GaitNet: An end-to-end network for gait based human identification

文献类型:期刊论文

AuthorSong, Chunfeng1; Huang, Yongzhen1; Huang, Yan1; Jia, Ning2; Wang, Liang1
SourcePATTERN RECOGNITION
Issued Date2019-12-01
Volume96Issue:106988Pages:11
KeywordGait recognition Video-based human identification End-to-end CNN Joint learning
ISSN0031-3203
DOI10.1016/j.patcog.2019.106988
English Abstract

Gait recognition is one of the most important techniques for human identification at a distance. Most current gait recognition frameworks consist of several separate steps: silhouette segmentation, feature extraction, feature learning, and similarity measurement. These modules are mutually independent with each part fixed, resulting in a suboptimal performance in challenging conditions. In this paper, we integrate those steps into one framework, i.e., an end-to-end network for gait recognition, named GaitNet. It is composed of two convolutional neural networks: one corresponds to gait segmentation, and the other corresponds to classification. The two networks are modeled in one joint learning procedure which can be trained jointly. This strategy greatly simplifies the traditional step-by-step manner and is thus much more efficient for practical applications. Moreover, joint learning can automatically adjust each part to fit the global optimal objective, leading to obvious performance improvement over separate learning. We evaluate our method on three large scale gait datasets, including CASIA-B, SZU RGB-D Gait and a newly built database with complex dynamic outdoor backgrounds. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results. The code and data will be released upon request. (C) 2019 Elsevier Ltd. All rights reserved.

Funding ProjectNational Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61806194] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010] ; National Key Research and Development Program of China[2016YEB1001000] ; CAS-AIR ; NVIDIA ; NVIDIA DGX-1 Al Supercomputer
WOS KeywordRECOGNITION ; REPRESENTATION ; IMAGE ; MODEL ; FRAMEWORK ; QUALITY
WOS Research AreaComputer Science ; Engineering
Language英语
PublisherELSEVIER SCI LTD
WOS IDWOS:000487569700017
源URL[http://ir.ia.ac.cn/handle/173211/26438]  
Collection中国科学院自动化研究所
Corresponding AuthorWang, Liang
Affiliation1.Chinese Acad Sci, Univ Chinese Acad Sci, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp,Inst Automat, Beijing 100190, Peoples R China
2.Univ Durham, Durham DH1 3LE, England
Recommended Citation
GB/T 7714
Song, Chunfeng,Huang, Yongzhen,Huang, Yan,et al. GaitNet: An end-to-end network for gait based human identification[J]. PATTERN RECOGNITION,2019,96(106988):11.
APA Song, Chunfeng,Huang, Yongzhen,Huang, Yan,Jia, Ning,&Wang, Liang.(2019).GaitNet: An end-to-end network for gait based human identification.PATTERN RECOGNITION,96(106988),11.
MLA Song, Chunfeng,et al."GaitNet: An end-to-end network for gait based human identification".PATTERN RECOGNITION 96.106988(2019):11.

入库方式: OAI收割

来源:自动化研究所

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