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Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition [article]

Xikun Zhang, Chang Xu, Xinmei Tian, Dacheng Tao
2018 arXiv   pre-print
A graph edge convolutional neural network is then designed for skeleton based action recognition.  ...  This paper investigates body bones from skeleton data for skeleton based action recognition.  ...  Related Work In this section, we will briefly review related works on skeleton based action recognition and graph convolutional neural networks.  ... 
arXiv:1805.06184v2 fatcat:vzmdrivyqjg6tddi4obigir3t4

Multi-filter dynamic graph convolutional networks for skeleton-based action recognition

Yating Yuan, Bo Yu, Wei Wang, Bihui Yu
2021 Procedia Computer Science  
proposes an Inception structure and dynamic skeleton diagram based on the convolutional neural network, namely, the multi-filter dynamic graph convolutional neural network.  ...  proposes an Inception structure and dynamic skeleton diagram based on the convolutional neural network, namely, the multi-filter dynamic graph convolutional neural network.  ...  The title of the project is Research on voice print recognition and Character Action Recognition of Network Culture service.  ... 
doi:10.1016/j.procs.2021.02.099 fatcat:sbws5wfz7bamfomqeaqhbmtvny

GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition

Wensong Chan, Zhiqiang Tian, Yang Wu
2020 Sensors  
Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs).  ...  In this paper, we propose an action-specific graph convolutional module, which is able to extract the implicit connections and properly balance them for each action.  ...  network for skeleton-based action recognition.  ... 
doi:10.3390/s20123499 pmid:32575802 fatcat:aczc3nt3ivetxnztys27i5q77y

Whole and Part Adaptive Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition

Qi Zuo, Lian Zou, Cien Fan, Dongqian Li, Hao Jiang, Yifeng Liu
2020 Sensors  
Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years.  ...  and the part graph convolution network (PGCN).  ...  We propose two new graph convolution methods for skeleton-based action recognition based on the movement patterns of human action: whole graph convolution (WGCN) and part graph convolution (PGCN), which  ... 
doi:10.3390/s20247149 pmid:33322231 fatcat:dgvzytru6rdfjb4ypbqhc3nika

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition [article]

Sijie Yan, Yuanjun Xiong, Dahua Lin
2018 arXiv   pre-print
Dynamics of human body skeletons convey significant information for human action recognition.  ...  In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning  ...  Conclusion In this paper, we present a novel model for skeleton based action recognition, the spatial temporal graph convolutional networks (ST-GCN).  ... 
arXiv:1801.07455v2 fatcat:mfhb2w4gqvhs7nq3sbs4cak3jq

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Sijie Yan, Yuanjun Xiong, Dahua Lin
2018 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Dynamics of human body skeletons convey significant information for human action recognition.  ...  In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning  ...  Conclusion In this paper, we present a novel model for skeleton based action recognition, the spatial temporal graph convolutional networks (ST-GCN).  ... 
doi:10.1609/aaai.v32i1.12328 fatcat:n5ljb3znureodellj4auyu5oqi

Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition [article]

Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu
2019 arXiv   pre-print
In this work, we propose a novel two-stream adaptive graph convolutional network (2s-AGCN) for skeleton-based action recognition.  ...  In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance.  ...  strategies for skeleton-based action recognition.  ... 
arXiv:1805.07694v3 fatcat:5ev3nthavngg3pbiobdfryngz4

Temporal Extension Module for Skeleton-Based Action Recognition [article]

Yuya Obinata, Takuma Yamamoto
2020 arXiv   pre-print
We present a module that extends the temporal graph of a graph convolutional network (GCN) for action recognition with a sequence of skeletons.  ...  In this work, we focus on adding connections to neighboring multiple vertices on the inter-frame and extracting additional features based on the extended temporal graph.  ...  Spatial temporal graph convolutional networks (ST-GCN) [7] is the first method to use a graph convolutional neural network for action recognition with a sequence of skeletons.  ... 
arXiv:2003.08951v2 fatcat:psflwyojtbhbxfhxslcr4ywgea

Remarkable Skeleton Based Human Action Recognition

Sushma Jaiswal, Tarun Jaiswal
2020 Artificial Intelligence Evolution  
Skeleton-based human-action-recognition (SBHAR) has wide applications in cognitive science and automatic surveillance.  ...  In this paper, we first highlight the need for action recognition and significance of 3D skeleton data and finally, we survey the largest 3D skeleton dataset, i.e.  ...  In this section, we provide extensive work related to skeleton human action recognition. Synergetic Graph Neural Network (Sym-GNN).  ... 
doi:10.37256/aie.122020562 fatcat:2wdzis5ax5bdfnwdhlzgvoh6xu

Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features

Jiuqing Dong, Yongbin Gao, Hyo Jong Lee, Heng Zhou, Yifan Yao, Zhijun Fang, Bo Huang
2020 Applied Sciences  
Recent studies have shown that the graph convolutional neural network works well in the action recognition task using spatial and temporal features of skeleton data.  ...  Furthermore, it can also effectively improve the robustness of the action recognition. Graph convolutional networks have been implemented on those skeletal data to recognize actions.  ...  Acknowledgments: We thank LEE from Jeonbuk National University for his great help. We also thank anonymous reviewers for their careful reading and insightful comments.  ... 
doi:10.3390/app10041482 fatcat:frha3ugas5djtcaytf26v37bdq

Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network [article]

Hao Xing, Darius Burschka
2022 arXiv   pre-print
In skeleton-based action recognition, Graph Convolutional Networks model human skeletal joints as vertices and connect them through an adjacency matrix, which can be seen as a local attention mask.  ...  However, in most existing Graph Convolutional Networks, the local attention mask is defined based on natural connections of human skeleton joints and ignores the dynamic relations for example between head  ...  CONCLUSION In this work, we develop a novel hybrid attention based graph neural network (HA-GCN) with new designed graph for skeleton-based human action recognition.  ... 
arXiv:2207.05493v1 fatcat:z4zgjodsvrfavadhfbhplcceju

Algebra Based Human Skeleton Sequence Reduction and Action Recognition [chapter]

Shibin Xuan, Kuan Wang, Lixia Liu, Chang Liu, Jiaxiang Li
2021 Frontiers in Artificial Intelligence and Applications  
Skeleton-based human action recognition is a research hotspot in recent years, but most of the research focuses on the spatio-temporal feature extraction by convolutional neural network.  ...  graph to improve the skeleton graph structure, and adding some virtual classes to disperse the error recognition rate.  ...  At present, deep learning models of skeleton-based human action recognition mainly include: Recurrent Neural Networks (RNN) [16, 17, 18, 19, 20] , Convolutional Neural Networks(CNN) [21, 22, 23] , and  ... 
doi:10.3233/faia210435 fatcat:ifkl5q4cvrgato3ytzslbhe2bu

Skeleton-Based Action Recognition With Directed Graph Neural Networks

Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
A novel directed graph neural network is designed specially to extract the information of joints, bones and their relationships and make prediction based on the extracted features.  ...  In existing methods, both the joint and bone information in skeleton data have been proved to be of great help for action recognition tasks.  ...  The most widely used models in deep-learning-based methods are recurrent neural networks (RNNs), convolutional neural networks (CNNs) and graph convolutional networks (GCNs), where the coordinates of joints  ... 
doi:10.1109/cvpr.2019.00810 dblp:conf/cvpr/ShiZCL19 fatcat:wektr5p25zdl3me3k7k5lyygxm

Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition [article]

Jinfeng Wei, Yunxin Wang, Mengli Guo, Pei Lv, Xiaoshan Yang, Mingliang Xu
2021 arXiv   pre-print
Graph convolutional networks (GCNs) based methods have achieved advanced performance on skeleton-based action recognition task.  ...  In this work, we propose a novel dynamic hypergraph convolutional networks (DHGCN) for skeleton-based action recognition.  ...  neural network for skeleton based action recognition.  ... 
arXiv:2112.10570v1 fatcat:x2564jl4dngmhba7duxf5yai6m

Multi‐stream adaptive spatial‐temporal attention graph convolutional network for skeleton‐based action recognition

Lubin Yu, Lianfang Tian, Qiliang Du, Jameel Ahmed Bhutto
2021 IET Computer Vision  
Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non-Euclidean graphs and achieve significant performance in skeleton-based action recognition.  ...  Skeleton-based action recognition algorithms have been widely applied to human action recognition.  ...  [12] used multiscale residual networks and several data enhancement strategies for skeleton-based action recognition.  ... 
doi:10.1049/cvi2.12075 fatcat:auyz7ymr3fevhphfk32qzwmqae
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