.png){kind=icon}
返回
Spatio-temporal stacking model for skeleton-based action recognition
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-021-02994-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the prevalence of affordable depth sensors, skeleton-based action recognition has attracted much attention as a significant computer vision task. The state-of-the-art recognition precision usually comes from the complicated deep learning networks which need a large quantity of training data. On the counterparts, none-deep learning methods are easy to be trained and understood, however, have restricted expressive ability to extract the spatial-temporal features of skeleton data simultaneously. Therefore, it is a challenging problem to use shallow learning architecture to effectively identify complex actions in skeleton data. In this paper, we first combine Temporal Hierarchy Pyramid (THP) and Symmetric Positive Definite (SPD) features to simultaneously capture the temporal relationship of inter-frame and the spatial relationship of intra-frame. Then, to achieve the same learning ability as the deep learning network for a non-linear system, we propose a novel stacking ensemble-based method to effectively identify complex actions in skeleton data. We carry out extensive verification of our method on widely used 3D action recognition datasets. The experiment results indicate that we achieve state-of-the-art performance on all compared datasets.
Keyword:
Action recognition
Skeleton data
Stacking model
Ensemble learning
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
暂无机构信息
引用论文
Polarizability characteristics of twisted bilayer graphene quantum dots in the absence of periodic moiré potential.png)
RSC Advances
OAAI
RSC AdvancesIF0
OAAI分享收藏Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing
PREAI
分享收藏
分享收藏
查看更多内容