Beyond Joints: Learning Representations From Primitive Geometries for Skeleton-Based Action Recognition and Detection | |
Wang Hongsong(王洪松)1,2,3,4; Wang Liang(王亮)1,2,3,4 | |
刊名 | IEEE Transactions on Image Processing |
2018 | |
卷号 | 27期号:9页码:4382 - 4394 |
关键词 | Skeleton-based Action Recognition Geometric Relations Viewpoint Transformation Action Detection |
英文摘要 | Recently, skeleton-based action recognition becomes popular owing to the development of cost-effective depth sensors and fast pose estimation algorithms. Traditional methods based on pose descriptors often fail on large-scale datasets due to the limited representation of engineered features. Recent recurrent neural networks (RNN) based approaches mostly focus on the temporal evolution of body joints and neglect the geometric relations. In this paper, we aim to leverage the geometric relations among joints for action recognition. We introduce three primitive geometries: joints, edges and surfaces. Accordingly, a generic end-to-end RNN based network is designed to accommodate the three inputs. For action recognition, a novel viewpoint transformation layer and temporal dropout layers are utilized in the RNN based network to learn robust representations. And for action detection, we first perform frame-wise action classification, then exploit a novel multi-scale sliding window algorithm. Experiments on the large-scale 3D action recognition benchmark datasets show that joints, edges and surfaces are effective and complementary for different actions. Our approaches dramatically outperform the existing state-of-the-art methods for both tasks of action recognition and action detection. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/21061] |
专题 | 自动化研究所_智能感知与计算研究中心 |
作者单位 | 1.Center for Research on Intelligent Perception and Computing (CRIPAC) 2.National Laboratory of Pattern Recognition (NLPR) 3.Institute of Automation, Chinese Academy of Sciences 4.University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Wang Hongsong,Wang Liang. Beyond Joints: Learning Representations From Primitive Geometries for Skeleton-Based Action Recognition and Detection[J]. IEEE Transactions on Image Processing,2018,27(9):4382 - 4394. |
APA | Wang Hongsong,&Wang Liang.(2018).Beyond Joints: Learning Representations From Primitive Geometries for Skeleton-Based Action Recognition and Detection.IEEE Transactions on Image Processing,27(9),4382 - 4394. |
MLA | Wang Hongsong,et al."Beyond Joints: Learning Representations From Primitive Geometries for Skeleton-Based Action Recognition and Detection".IEEE Transactions on Image Processing 27.9(2018):4382 - 4394. |
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