Skeleton-Based Action Recognition with Directed Graph Neural Networks | |
Shi L(史磊)2,3; Zhang YF(张一帆)2,3; Cheng J(程健)1,2,3; Lu HQ(卢汉清)2,3 | |
2019 | |
会议日期 | June 16, 2019 - June 20, 2019 |
会议地点 | Long Beach, CA, United states |
英文摘要 | The skeleton data have been widely used for the action recognition tasks since they can robustly accommodate dynamic circumstances and complex backgrounds. In existing methods, both the joint and bone information in skeleton data have been proved to be of great help for action recognition tasks. However, how to incorporate these two types of data to best take advantage of the relationship between joints and bones remains a problem to be solved. In this work, we represent the skeleton data as a directed acyclic graph based on the kinematic dependency between the joints and bones in the natural human body. A novel directed graph neural network is designed specially to extract the information of joints, bones and their relations and make prediction based on the extracted features. In addition, to better fit the action recognition task, the topological structure of the graph is made adaptive based on the training process, which brings notable improvement. Moreover, the motion information of the skeleton sequence is exploited and combined with the spatial information to further enhance the performance in a two-stream framework. Our final model is tested on two large-scale datasets, NTU-RGBD and Skeleton-Kinetics, and exceeds state-of-the-art performance on both of them. |
源文献作者 | IEEE Computer Society |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44363] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
通讯作者 | Zhang YF(张一帆) |
作者单位 | 1.University of Chinese Academy of Sciences 2.CAS Center for Excellence in Brain Science and Intelligence Technology 3.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Shi L,Zhang YF,Cheng J,et al. Skeleton-Based Action Recognition with Directed Graph Neural Networks[C]. 见:. Long Beach, CA, United states. June 16, 2019 - June 20, 2019. |
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