Knowledge Commons of Institute of Automation,CAS
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition | |
Shi, Lei1,2; Zhang, Yifan1,2; Cheng, Jian1,2,3; Lu, Hanqing1,2 | |
2019-06 | |
会议名称 | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
会议日期 | June 16, 2019 - June 20, 2019 |
会议地点 | Long Beach, CA, United states |
出版者 | IEEE Computer Society |
摘要 |
In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance. However, in existing GCN-based methods, the topology of the graph is set manually, and it is fixed over all layers and input samples. This may not be optimal for the hierarchical GCN and diverse samples in action recognition tasks. In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods. In this work, we propose a novel two-stream adaptive graph convolutional network (2s-AGCN) for skeleton-based action recognition. The topology of the graph in our model can be either uniformly or individually learned by the BP algorithm in an end-to-end manner. This data-driven method increases the flexibility of the model for graph construction and brings more generality to adapt to various data samples. Moreover, a two-stream framework is proposed to model both the first-order and the second-order information simultaneously, which shows notable improvement for the recognition accuracy. Extensive experiments on the two large-scale datasets, NTU-RGBD and Kinetics-Skeleton, demonstrate that the performance of our model exceeds the state-of-the-art with a significant margin. |
收录类别 | SCI |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42211 |
专题 | 复杂系统认知与决策实验室_高效智能计算与学习 紫东太初大模型研究中心_图像与视频分析 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China 2.CAS Center for Excellence in Brain Science and Intelligence Technology, China 3.University of Chinese Academy of Sciences, China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Shi, Lei,Zhang, Yifan,Cheng, Jian,et al. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition[C]:IEEE Computer Society,2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Shi_Two-Stream_Adapt(691KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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