Pose-Appearance Relational Modeling for Video Action Recognition

doi:10.1109/TIP.2022.3228156

	Pose-Appearance Relational Modeling for Video Action Recognition
	Cui, Mengmeng 1; Wang, Wei1 ; Zhang, Kunbo1,2 ; Sun, Zhenan1,2 ; Wang, Liang1,2
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
	2023
卷号	32 页码:295-308
通讯作者	Wang, Wei(wangwei@nlpr.ia.ac.cn)
摘要	Recent studies of video action recognition can be classified into two categories: the appearance-based methods and the pose-based methods. The appearance-based methods generally cannot model temporal dynamics of large motion well by virtue of optical flow estimation, while the pose-based methods ignore the visual context information such as typical scenes and objects, which are also important cues for action understanding. In this paper, we tackle these problems by proposing a Pose-Appearance Relational Network (PARNet), which models the correlation between human pose and image appearance, and combines the benefits of these two modalities to improve the robustness towards unconstrained real-world videos. There are three network streams in our model, namely pose stream, appearance stream and relation stream. For the pose stream, a Temporal Multi-Pose RNN module is constructed to obtain the dynamic representations through temporal modeling of 2D poses. For the appearance stream, a Spatial Appearance CNN module is employed to extract the global appearance representation of the video sequence. For the relation stream, a Pose-Aware RNN module is built to connect pose and appearance streams by modeling action-sensitive visual context information. Through jointly optimizing the three modules, PARNet achieves superior performances compared with the state-of-the-arts on both the pose-complete datasets (KTH, Penn-Action, UCF11) and the challenging pose-incomplete datasets (UCF101, HMDB51, JHMDB), demonstrating its robustness towards complex environments and noisy skeletons. Its effectiveness on NTU-RGBD dataset is also validated even compared with 3D skeleton-based methods. Furthermore, an appearance-enhanced PARNet equipped with a RGB-based I3D stream is proposed, which outperforms the Kinetics pre-trained competitors on UCF101 and HMDB51. The better experimental results verify the potentials of our framework by integrating various modules.
关键词	Action recognition 2D pose-appearance relational modeling temporal attention LSTM
DOI	10.1109/TIP.2022.3228156
关键词[WOS]	ATTENTION NETWORK ; LSTM
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61976214] ; National Natural Science Foundation of China[62071468] ; National Natural Science Foundation of China[62006225] ; National Natural Science Foundation of China[61806197] ; National Natural Science Foundation of China[6207146] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27040700] ; Beijing Municipal Natural Science Foundation[4214075]
项目资助者	National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Beijing Municipal Natural Science Foundation
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000902111900021
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[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/173211/51085
专题	多模态人工智能系统全国重点实验室
通讯作者	Wang, Wei
作者单位	1.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
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	Cui, Mengmeng,Wang, Wei,Zhang, Kunbo,et al. Pose-Appearance Relational Modeling for Video Action Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:295-308.
APA	Cui, Mengmeng,Wang, Wei,Zhang, Kunbo,Sun, Zhenan,&Wang, Liang.(2023).Pose-Appearance Relational Modeling for Video Action Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,295-308.
MLA	Cui, Mengmeng,et al."Pose-Appearance Relational Modeling for Video Action Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):295-308.