Frame-GAN: Increasing the frame rate of gait videos with generative adversarial networks

doi:10.1016/j.neucom.2019.11.015

CASIA OpenIR > 模式识别实验室

	Frame-GAN: Increasing the frame rate of gait videos with generative adversarial networks
	Xue, Wei1,2 ; Ai, Hong 1; Sun, Tianyu 2; Song, Chunfeng2,3 ; Huang, Yan2,3 ; Wang, Liang2,3
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-03-07
卷号	380 页码:95-104
通讯作者	Ai, Hong(aihong@hrbust.edu.cn)
摘要	Most existing methods of identifying person except gait recognition require the cooperation of the subjects. Aiming at detecting the pattern of human walking movement, gait recognition takes advantage of the time-serial data and can identify a person distantly. The time-serial data, which is usually presented in video form, always has a limitation in frame rate, which intrinsically affects the performance of the recognition models. In order to increase the frame rate of gait videos, we propose a new kind of generative adversarial networks (GAN) named Frame-GAN to reduce the gap between adjacent frames. Inspired by the recent advances in metric learning, we also propose a new effective loss function named Margin Ratio Loss (MRL) to boost the recognition model. We evaluate the proposed method on the challenging CASIA-B and OU-ISIR gait databases. Extensive experimental results show that the proposed Frame-GAN and MRL are effective. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Gait recognition Generative adversarial networks Metric learning Deep learning
DOI	10.1016/j.neucom.2019.11.015
关键词[WOS]	RECOGNITION ; IMAGE
收录类别	SCI
语种	英语
资助项目	CAS-AIR ; NVIDIA DGX-1 AI Supercomputer ; NVIDIA ; 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[61420106015] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61525306] ; National Key Research and Development Program of China[2016YFB1001000] ; National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61806194] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010] ; NVIDIA ; NVIDIA DGX-1 AI Supercomputer ; CAS-AIR
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Capital Science and Technology Leading Talent Training Project ; Beijing Science and Technology Project ; NVIDIA ; NVIDIA DGX-1 AI Supercomputer ; CAS-AIR
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000507986500010
出版者	ELSEVIER
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：9[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/173211/29542
专题	模式识别实验室
通讯作者	Ai, Hong
作者单位	1.Harbin Univ Sci & Technol, Sch Automat, Harbin 150001, Peoples R China 2.Chinese Acad Sci CASIA, CRIPAC, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 3.UCAS, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Xue, Wei,Ai, Hong,Sun, Tianyu,et al. Frame-GAN: Increasing the frame rate of gait videos with generative adversarial networks[J]. NEUROCOMPUTING,2020,380:95-104.
APA	Xue, Wei,Ai, Hong,Sun, Tianyu,Song, Chunfeng,Huang, Yan,&Wang, Liang.(2020).Frame-GAN: Increasing the frame rate of gait videos with generative adversarial networks.NEUROCOMPUTING,380,95-104.
MLA	Xue, Wei,et al."Frame-GAN: Increasing the frame rate of gait videos with generative adversarial networks".NEUROCOMPUTING 380(2020):95-104.