Two-Steam Fully Connected Graph Convolutional Network for Skeleton-Based Action Recognition
【摘要】:Skeleton-based human action recognition has recently drawn a lot of attentions with the increasing availability of large-scale skeleton datasets.Graph Convolutional Network(GCN) methods have achieved relatively good performances in action recognition.However,most GCN methods based on predefined graphs with fixed topology constraints always neglect the potential dependencies derived from the cooperative movement of all joints.Besides,the lengths and the directions of skeletons are rarely involved.These easily cause a larger deviation of the estimated action from the actual action.Here,a two-steam fully connected graph convolutional network(2 s-FGCN) is proposed.The topology structure of the 2 s-FGCN covers the local physical connections and the global potential cooperation of all joints and the joints,lengths and directions of skeletons are all input to the model.The experimental results on two datasets(NTU-RGB+D and Kinetics-Skeleton) demonstrate that the proposed model can obtain the state-of-the-art results.
|
|
|
|
1 |
Tsering Shrestha;[D];湖南大学;2006年 |
2 |
巴雅尔;[D];内蒙古师范大学;2012年 |
3 |
Antoine GAMBI;[D];北京邮电大学;2017年 |
4 |
Boakye-Yiadom Adwoa Agyeiwaa;[D];西南科技大学;2020年 |
5 |
Hamza Mehdi Khan;基于神经网络的SAR图像船舶识别[D];上海交通大学;2019年 |
6 |
温研东;基于稀疏表示与深度学习的人脸识别研究[D];华南理工大学;2016年 |
7 |
Kim JungTae(金正泰);[D];华东理工大学;2015年 |
8 |
郭姗姗;澳大利亚职业学校先前学习认定研究[D];西南大学;2008年 |
9 |
郑献春;基于梯度导数的人脸识别的研究与实现[D];西安电子科技大学;2011年 |
|