Enhanced discriminative graph convolutional network with adaptive temporal modelling for skeleton-based action recognition

2022 ◽  
pp. 103348
Author(s):  
Tamam Alsarhan ◽  
Usman Ali ◽  
Hongtao Lu
Keyword(s):  
Action Recognition ◽  
Convolutional Network ◽  
Temporal Modelling

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.

Video Action Recognition Based on Hybrid Convolutional Network

2020 ◽  
pp. 451-462
Author(s):  
Yanyan Song ◽  
Li Tan ◽  
Lina Zhou ◽  
Xinyue Lv ◽  
Zihao Ma
Keyword(s):  
Action Recognition ◽  
Convolutional Network

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

scholarly journals Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching

2020 ◽  
Vol 34 (03) ◽  
pp. 2669-2676 ◽  
Author(s):  
Wei Peng ◽  
Xiaopeng Hong ◽  
Haoyu Chen ◽  
Guoying Zhao
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Order Approximation ◽  
Human Action Recognition ◽  
Search Space ◽  
Human Action ◽  
Higher Order ◽  
Dynamic Graph ◽  
Convolutional Network ◽  
Representational Capacity

Human action recognition from skeleton data, fuelled by the Graph Convolutional Network (GCN) with its powerful capability of modeling non-Euclidean data, has attracted lots of attention. However, many existing GCNs provide a pre-defined graph structure and share it through the entire network, which can loss implicit joint correlations especially for the higher-level features. Besides, the mainstream spectral GCN is approximated by one-order hop such that higher-order connections are not well involved. All of these require huge efforts to design a better GCN architecture. To address these problems, we turn to Neural Architecture Search (NAS) and propose the first automatically designed GCN for this task. Specifically, we explore the spatial-temporal correlations between nodes and build a search space with multiple dynamic graph modules. Besides, we introduce multiple-hop modules and expect to break the limitation of representational capacity caused by one-order approximation. Moreover, a corresponding sampling- and memory-efficient evolution strategy is proposed to search in this space. The resulted architecture proves the effectiveness of the higher-order approximation and the layer-wise dynamic graph modules. To evaluate the performance of the searched model, we conduct extensive experiments on two very large scale skeleton-based action recognition datasets. The results show that our model gets the state-of-the-art results in term of given metrics.

scholarly journals Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qiubo Zhong ◽  
Caiming Zheng ◽  
Haoxiang Zhang
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Sequence Data ◽  
Spatial Relationship ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Convolutional Network ◽  
Action Sequence ◽  
Interaction System ◽  
High Discrimination

A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.

Multi-scale temporal feature-based dense convolutional network for action recognition

2020 ◽  
Vol 29 (06) ◽  
Author(s):  
Xiaoqiang Li ◽  
Miao Xie ◽  
Yin Zhang ◽  
Jide Li
Keyword(s):  
Action Recognition ◽  
Convolutional Network ◽  
Multi Scale ◽  
Feature Based ◽  
Temporal Feature

Graph convolutional network with structure pooling and joint-wise channel attention for action recognition

2020 ◽  
Vol 103 ◽  
pp. 107321 ◽  
Author(s):  
Yuxin Chen ◽  
Gaoqun Ma ◽  
Chunfeng Yuan ◽  
Bing Li ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Convolutional Network

Action Recognition by 3D Convolutional Network

2018 ◽  
Author(s):  
Matus Brezovsky ◽  
Dominik Sopiak ◽  
Milos Oravec
Keyword(s):  
Action Recognition ◽  
Convolutional Network
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