Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
文献类型:会议论文
作者 | Song, Yi-Fan1,2,3![]() ![]() ![]() |
出版日期 | 2019-09 |
会议日期 | 2019.09.22 -- 2019.09.25 |
会议地点 | Taipei, Taiwan, China |
关键词 | Action Recognition Skeleton Data Graph Convolutional Network Activation Maps Occlusion |
DOI | 10.1109/ICIP.2019.8802917 |
英文摘要 | Current methods for skeleton-based human action recognition usually work with completely observed skeletons. However, in real scenarios, it is prone to capture incomplete and noisy skeletons, which will deteriorate the performance of traditional models. To enhance the robustness of action recognition models to incomplete skeletons, we propose a multi-stream graph convolutional network (GCN) for exploring sufficient discriminative features distributed over all skeleton joints. Here, each stream of the network is only responsible for learning features from currently unactivated joints, which are distinguished by the class activation maps (CAM) obtained by preceding streams, so that the activated joints of the proposed method are obviously more than traditional methods. Thus, the proposed method is termed richly activated GCN (RA-GCN), where the richly discovered features will improve the robustness of the model. Compared to the state-of-the-art methods, the RA-GCN achieves comparable performance on the NTU RGB+D dataset. Moreover, on a synthetic occlusion dataset, the performance deterioration can be alleviated by the RA-GCN significantly. |
语种 | 英语 |
URL标识 | 查看原文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44957] ![]() |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Song, Yi-Fan |
作者单位 | 1.Center for Research on Intelligent Perception and Computing (CRIPAC) 2.Institute of Automation, Chinese Academy of Sciences (CASIA) 3.School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS) |
推荐引用方式 GB/T 7714 | Song, Yi-Fan,Zhang, Zhang,Wang, Liang. Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons[C]. 见:. Taipei, Taiwan, China. 2019.09.22 -- 2019.09.25. |
入库方式: OAI收割
来源:自动化研究所
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