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

2021 ◽  
Author(s):  
Lubin Yu ◽  
Lianfang Tian ◽  
Qiliang Du ◽  
Jameel Ahmed Bhutto
Keyword(s):  
Action Recognition ◽  
Temporal Attention ◽  
Convolutional Network

scholarly journals Relation Selective Graph Convolutional Network for Skeleton-Based Action Recognition

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2275
Author(s):  
Wenjie Yang ◽  
Jianlin Zhang ◽  
Jingju Cai ◽  
Zhiyong Xu
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Recognition Task ◽  
Context Modeling ◽  
Selection Mechanism ◽  
Temporal Attention ◽  
Significant Progress ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Art Performance

Graph convolutional networks (GCNs) have made significant progress in the skeletal action recognition task. However, the graphs constructed by these methods are too densely connected, and the same graphs are used repeatedly among channels. Redundant connections will blur the useful interdependencies of joints, and the overly repetitive graphs among channels cannot handle changes in joint relations between different actions. In this work, we propose a novel relation selective graph convolutional network (RS-GCN). We also design a trainable relation selection mechanism. It encourages the model to choose solid edges to work and build a stable and sparse topology of joints. The channel-wise graph convolution and multiscale temporal convolution are proposed to strengthening the model’s representative power. Furthermore, we introduce an asymmetrical module named the spatial-temporal attention module for more stable context modeling. Combining those changes, our model achieves state-of-the-art performance on three public benchmarks, namely NTU-RGB+D, NTU-RGB+D 120, and Northwestern-UCLA.

scholarly journals NPU RGB+D Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players

2021 ◽  
Vol 11 (10) ◽  
pp. 4426
Author(s):  
Chunyan Ma ◽  
Ji Fan ◽  
Jinghao Yao ◽  
Tao Zhang
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Evaluation Criteria ◽  
Image Data ◽  
Basketball Player ◽  
Basketball Players ◽  
Convolutional Network ◽  
Atomic Actions ◽  
New Feature

Computer vision-based action recognition of basketball players in basketball training and competition has gradually become a research hotspot. However, owing to the complex technical action, diverse background, and limb occlusion, it remains a challenging task without effective solutions or public dataset benchmarks. In this study, we defined 32 kinds of atomic actions covering most of the complex actions for basketball players and built the dataset NPU RGB+D (a large scale dataset of basketball action recognition with RGB image data and Depth data captured in Northwestern Polytechnical University) for 12 kinds of actions of 10 professional basketball players with 2169 RGB+D videos and 75 thousand frames, including RGB frame sequences, depth maps, and skeleton coordinates. Through extracting the spatial features of the distances and angles between the joint points of basketball players, we created a new feature-enhanced skeleton-based method called LSTM-DGCN for basketball player action recognition based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods. Many advanced action recognition methods were evaluated on our dataset and compared with our proposed method. The experimental results show that the NPU RGB+D dataset is very competitive with the current action recognition algorithms and that our LSTM-DGCN outperforms the state-of-the-art action recognition methods in various evaluation criteria on our dataset. Our action classifications and this NPU RGB+D dataset are valuable for basketball player action recognition techniques. The feature-enhanced LSTM-DGCN has a more accurate action recognition effect, which improves the motion expression ability of the skeleton data.

Human Action Recognition Combining Sequential Dynamic Images and Two-Stream Convolutional Network

2021 ◽  
Vol 58 (2) ◽  
pp. 0210007
Author(s):  
张文强 Zhang Wenqiang ◽  
王增强 Wang Zengqiang ◽  
张良 Zhang Liang
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Convolutional Network
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