Knowledge Commons of Institute of Automation,CAS
3D PostureNet: A unified framework for skeleton-based posture recognition | |
Liu, Jianbo1,2; Wang, Ying1; Liu, Yongcheng1,2; Xiang, Shiming1; Pan, Chunhong1 | |
发表期刊 | PATTERN RECOGNITION LETTERS |
ISSN | 0167-8655 |
2020-12-01 | |
卷号 | 140期号:140页码:143-149 |
摘要 | Image-based posture recognition is a very challenging problem as it is difficult to acquire rich 3D information from postures in 2D images. Existing methods founded on 3D skeleton cues could alleviate this issue, but they are not particularly efficient due to the application of handcrafted features and traditional classifiers. This paper presents a novel and unified framework for skeleton-based posture recognition, applying powerful 3D Convolutional Neural Network (CNN) to this issue. Technically, bounding-box-based normalization for the raw skeleton data is proposed to eliminate the coordinate differences caused by diverse recording environments and posture displacements. Moreover, Gaussian voxelization for the skeleton is employed to expressively represent the posture configuration. Thereby, an end-to-end framework based on 3D CNN, called 3D PostureNet, is developed for robust posture recognition. To verify its effectiveness, a large-scale writing posture dataset is created and released in this work, including 113,400 samples of 30 subjects with 15 postures. Extensive experiments on the public MSRA hand gesture dataset, body pose dataset and the proposed writing posture dataset demonstrate that 3D PostureNet achieves significantly superior performance on both skeleton-based human posture and hand posture recognition tasks. (C) 2020 Elsevier B.V. All rights reserved. |
关键词 | Human posture recognition Static hand gesture recognition Skeleton-based 3D convolutional neural network |
DOI | 10.1016/j.patrec.2020.09.029 |
关键词[WOS] | SYSTEM |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Major Project for New Generation of AI[2018AAA0100400] ; National Key Research and Development Program[2016YFB0501100] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61976208] |
项目资助者 | Major Project for New Generation of AI ; National Key Research and Development Program ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000595366500001 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42705 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 中国科学院自动化研究所 |
通讯作者 | Wang, Ying |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, 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 | Liu, Jianbo,Wang, Ying,Liu, Yongcheng,et al. 3D PostureNet: A unified framework for skeleton-based posture recognition[J]. PATTERN RECOGNITION LETTERS,2020,140(140):143-149. |
APA | Liu, Jianbo,Wang, Ying,Liu, Yongcheng,Xiang, Shiming,&Pan, Chunhong.(2020).3D PostureNet: A unified framework for skeleton-based posture recognition.PATTERN RECOGNITION LETTERS,140(140),143-149. |
MLA | Liu, Jianbo,et al."3D PostureNet: A unified framework for skeleton-based posture recognition".PATTERN RECOGNITION LETTERS 140.140(2020):143-149. |
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3D PostureNet_A unif(1997KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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