Skeleton-Based Action Recognition With Gated Convolutional Neural Networks

doi:10.1109/TCSVT.2018.2879913

	Skeleton-Based Action Recognition With Gated Convolutional Neural Networks
	Cao, Congqi1 ; Lan, Cuiling 2; Zhang, Yifan1,3,4 ; Zeng, Wenjun 2; Lu, Hanqing3,4 ; Zhang, Yanning
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN	1051-8215
	2019-11-01
卷号	29 期号:11 页码:3247-3257
通讯作者	Cao, Congqi(congqi.cao@nwpu.edu.cn) ; Lan, Cuiling(culan@microsoft.com) ; Zhang, Yifan(yfzhang@nlpr.ia.ac.cn)
摘要	For skeleton-based action recognition, most of the existing works used recurrent neural networks. Using convolutional neural networks (CNNs) is another attractive solution considering their advantages in parallelization, effectiveness in feature learning, and model base sufficiency. Besides these, skeleton data are low-dimensional features. It is natural to arrange a sequence of skeleton features chronologically into an image, which retains the original information. Therefore, we solve the sequence learning problem as an image classification task using CNNs. For better learning ability, we build a classification network with stacked residual blocks and having a special design called linear skip gated connection which can benefit information propagation across multiple residual blocks. When arranging the coordinates of body joints in one frame into a skeleton feature, we systematically investigate the performance of part-based, chain-based, and traversal-based orders. Furthermore, a fully convolutional permutation network is designed to learn an optimized order for data rearrangement. Without any bells and whistles, our proposed model achieves state-of-the-art performance on two challenging benchmark datasets, outperforming existing methods significantly.
关键词	Skeleton Logic gates Task analysis Recurrent neural networks Matrix converters Three-dimensional displays Convolutional neural networks Skeleton action recognition gated connection convolutional neural networks
DOI	10.1109/TCSVT.2018.2879913
收录类别	SCI
语种	英语
资助项目	Fundamental Research Funds for the Central Universities[31020180QD138] ; Fundamental Research Funds for the Central Universities[31020180QD138]
项目资助者	Fundamental Research Funds for the Central Universities
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000494710600008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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：90[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/173211/28839
专题	复杂系统认知与决策实验室_高效智能计算与学习
通讯作者	Cao, Congqi; Lan, Cuiling; Zhang, Yifan
作者单位	1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China 2.Microsoft Res Asia, Beijing 100080, Peoples R China 3.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
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	Cao, Congqi,Lan, Cuiling,Zhang, Yifan,et al. Skeleton-Based Action Recognition With Gated Convolutional Neural Networks[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2019,29(11):3247-3257.
APA	Cao, Congqi,Lan, Cuiling,Zhang, Yifan,Zeng, Wenjun,Lu, Hanqing,&Zhang, Yanning.(2019).Skeleton-Based Action Recognition With Gated Convolutional Neural Networks.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,29(11),3247-3257.
MLA	Cao, Congqi,et al."Skeleton-Based Action Recognition With Gated Convolutional Neural Networks".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 29.11(2019):3247-3257.