CASIA OpenIR  > 智能感知与计算
GaitNet: An end-to-end network for gait based human identification
Song, Chunfeng1; Huang, Yongzhen1; Huang, Yan1; Jia, Ning2; Wang, Liang1
发表期刊PATTERN RECOGNITION
ISSN0031-3203
2019-12-01
卷号96期号:106988页码:11
摘要

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.

关键词Gait recognition Video-based human identification End-to-end CNN Joint learning
DOI10.1016/j.patcog.2019.106988
关键词[WOS]RECOGNITION ; REPRESENTATION ; IMAGE ; MODEL ; FRAMEWORK ; QUALITY
收录类别SCI
语种英语
资助项目NVIDIA DGX-1 Al Supercomputer ; NVIDIA ; CAS-AIR ; National Key Research and Development Program of China[2016YEB1001000] ; Beijing Science and Technology Project[Z181100008918010] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; National Natural Science Foundation of China[61806194] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61525306] ; National 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研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000487569700017
出版者ELSEVIER SCI LTD
七大方向——子方向分类生物特征识别
引用统计
被引频次:59[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/26439
专题智能感知与计算
通讯作者Wang, Liang
作者单位1.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
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
推荐引用方式
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.
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