An attentional spatial temporal graph convolutional network with co-occurrence feature learning for action recognition

Tian, Dong; Lu, Zhe‐Ming; Xiao, Chunxia; Ma, Longhua

doi:10.1007/s11042-020-08611-4

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…The hyperparameters are set mainly by grid search to determine the optimal parameters, keeping the other hyperparameters constant when determining a parameter. The batch_size is taken from the set [8,16,32,64,128,256], η is taken from the set [0.0001, 0.0005, 0.001, 0.005, 0.01], the optimizer is selected from [Adam, SGD, Adagrad]. Finally, the results are obtained through multiple experiments on the verification set.…”

Section: Analysis Of Stagcn-ieall (1) Parameter Settingmentioning

confidence: 99%

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IEALL: Dam Deformation Prediction Model Based on Combination Model Method

Jing

et al. 2023

Applied Sciences

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The accuracy of dam deformation prediction is a key issue that needs to be addressed due to the many factors that influence dam deformation. In this paper, a dam deformation prediction model based on IEALL (IGWO-EEMD-ARIMA-LSTM-LSTM) is proposed for a single-point scenario. The IEALL model is based on the idea of a combination model. Firstly, EEMD is used to decompose the dam deformation data, and then the ARIMA and LSTM models are selected for prediction. To address the problem of low prediction accuracy caused by simple linear addition of prediction results from different models in traditional combination models, the LSTM model is used to learn the combination relationship of different model prediction results. The problem of neural network parameters falling into local optima due to random initialization is addressed by using the improved gray wolf optimization (IGWO) to optimize multiple parameters in the IEALL combination model to obtain the optimal parameters. For the multi-point scenario of dam deformation, based on the IEALL model, a dam deformation prediction model based on spatio-temporal correlation and IEALL (STAGCN-IEALL) is proposed. This model introduces graph convolutional neural networks (GCN) to extract spatial features from multi-point sequences, increasing the model’s ability to express spatial dimensions. To address the dynamic correlation between different points in the deformation sequence at any time and the dynamic dependence on different points at any given time, spatio-temporal attention mechanisms are introduced to capture dynamic correlation from both spatial and temporal dimensions. Experimental results showed that compared to ST-GCN, IEALL reduced the RMSE, MAE, and MAPE by 16.06%, 14.72%, and 21.19%. Therefore, the proposed model effectively reduces the prediction error and can more accurately predict the trend of dam deformation changes.

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Section: Analysis Of Stagcn-ieall (1) Parameter Settingmentioning

confidence: 99%

“…ST-GCN [32]: Spatio-temporal graph convolutional neural networks, which combine graph convolutional layers and convolutional sequences to make predictions.…”

mentioning

confidence: 99%

IEALL: Dam Deformation Prediction Model Based on Combination Model Method

Jing

et al. 2023

Applied Sciences

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“…ST-GCN is a typical spatiotemporal approach since it performs GCN on spatiotemporal graph (STG) directly and therefore extracts spatiotemporal information simultaneously. Methods, such as [ 29 , 48 , 54 , 60 , 68 , 82 , 86 , 96 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 ] are all developed based on ST-GCN. Methods based on AGCN also work on STG, such as [ 66 , 73 , 93 , 114 ].…”

Section: A New Taxonomy For Skeleton-gnn-based Harmentioning

confidence: 99%

Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey

Feng

Meunier

2022

Sensors

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Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated on skeleton-graph-based approaches. Connecting the skeleton joints as in the physical appearance can naturally generate a graph. This paper provides an up-to-date review for readers on skeleton graph-neural-network-based human action recognition. After analyzing previous related studies, a new taxonomy for skeleton-GNN-based methods is proposed according to their designs, and their merits and demerits are analyzed. In addition, the datasets and codes are discussed. Finally, future research directions are suggested.

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“…In recent years, researchers have proposed many action classification methods based on neural network. The methods they used include the model based on RNN [9], CNN [10,11], GCN [12][13][14], and Long Short-Term Memory (LSTM) [15]. The probabilistic graphical model is one of the popular solutions to the problem of action classification.…”

Section: Human Action Classificationmentioning

confidence: 99%

A Lightweight Human Action Classification Method for Green IoT Sport Applications

Xiao

Huang

et al. 2022

Journal of Sensors

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This paper proposes a lightweight human action classification method for Green Internet of Things (IoT) sport applications. This method classifies the human motion data collected by wearables or other IoT devices with energy-efficient techniques, by enabling a small number of sample training and incremental classification to achieve the purpose of energy-efficient. To lessen the complexity of the model and reduce the number of samples required for parameter estimation, we propose a shared Hidden Conditional Random Field (sHCRF) model. The sHCRF model adds a shared-classification layer structure to reduce the parameter computation. In the experiments, the classification accuracy of the sHCRF model is above 95%. This paper introduces an incremental learning method based on knowledge distillation. The new model suppresses the forgetting of existing classification knowledge while fitting new data to learn new classification knowledge. In the incremental scenarios, the classification accuracy of the sHCRF model is above 70%. The experimental results show that this method can lightly implement convenient and fast automatic classification of action acquisition.

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An attentional spatial temporal graph convolutional network with co-occurrence feature learning for action recognition

Cited by 12 publications

References 25 publications

IEALL: Dam Deformation Prediction Model Based on Combination Model Method

IEALL: Dam Deformation Prediction Model Based on Combination Model Method

Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey

A Lightweight Human Action Classification Method for Green IoT Sport Applications

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