On the spatial attention in spatio-temporal graph convolutional networks for skeleton-based human action recognition

Heidari, Negar; Iosifidis, Alexandros

doi:10.1109/ijcnn52387.2021.9534440

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…In our previous work in, we presented a novel set of partitioning strategies that can capture more accurately the relationship between the joints of the skeleton that outperform the accuracy achieved of the baseline model in [8]. Additionally, multiple work has been done to use attention modules in the model to improve the performance [35,36].…”

Section: Related Workmentioning

confidence: 99%

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Alsawadi¹,

El-kenawy²,

Rio³

2023

Computers, Materials &Amp; Continua

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The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks. Hence, in this study, we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition. Moreover, we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor. Additionally, we propose different skeleton detection thresholds that can improve the accuracy performance even further. We reached a top-1 accuracy performance of 40.1% on the Kinetics dataset. For the NTU-RGB+D dataset, we achieved 87.59% and 92.1% accuracy for Cross-Subject and Cross-View evaluation criteria, respectively.

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Section: Related Workmentioning

confidence: 99%

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Alsawadi¹,

El-kenawy²,

Rio³

2023

Computers, Materials &Amp; Continua

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“…The authors' goal in [20] was to improve the performance of complex systems, where they proposed a hybridization of GWO with Artificial Bee Colony (ABC). GWOSCA, presented in [21] and combines GWO and the Sine Cosine Algorithm, is yet another hybrid technique (SCA). According to this research's findings, the hybrid approaches' performance was significantly superior to that of other global or local search methods.…”

Section: Metaheuristics Algorithms For Feature Selectionmentioning

confidence: 99%

Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

S. Alsawadi,

M. El-kenawy,

Rio

2023

Intelligent Automation &Amp; Soft Computing

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The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph's topology and then fix it across all input layers and samples. This research shows how to use the Statistical Fractal Search (SFS)-Guided whale optimization algorithm (GWOA). To get the best solution for the GWOA, we adopt the SFS diffusion algorithm, which uses the random walk with a Gaussian distribution method common to growing systems. Continuous values are transformed into binary to apply to the feature-selection problem in conjunction with the BlazePose skeletal topology and stochastic fractal search to construct a novel implementation of the BlazePose topology for action recognition. In our experiments, we employed the Kinetics and the NTU-RGB+D datasets. The achieved actiona accuracy in the X-View is 93.14% and in the X-Sub is 96.74%. In addition, the proposed model performs better in numerous statistical tests such as the Analysis of Variance (ANOVA), Wilcoxon signedrank test, histogram, and times analysis.

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“…On the other hand, the contribution of each feature to the action detection job varies depending on the dimension of the skeleton data, and some features even cause noise. e attention mechanism can be used to increase the performance and stability of a model by focusing on its most significant characteristics [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Big Data System for Dragon Boat Rowing Action Training Based on Multidimensional Stereo Vision

Tao

2022

Mathematical Problems in Engineering

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With the rapid advancement of artificial intelligence technology and the widespread use of sensing technology in education, human-computer interaction teaching has gradually developed in sports and education. Traditionally, teachers explain and demonstrate the fundamentals of movements first, then organize exercises, and students gradually consolidate technical movements through repetition. This process requires teachers to repeatedly explain, such that students can develop movement concepts, and to assist students in correcting their movements through practice. Eventually, students can master the dragon boat’s paddling movements. Teachers frequently struggle to observe all of their students’ movements and are therefore unable to correct them in a timely and effective manner. To address the aforementioned issues, this paper proposes a big data system for multidimensional stereo vision training in dragon boat paddling action. The stock price action recognition of dragon boat paddles and the movement of students’ dragon boat paddles are realized through the multidimensional fusion of attention mechanism and spatiotemporal graph convolution. Make judgments to more effectively guide and train students’ paddling movements.

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On the spatial attention in spatio-temporal graph convolutional networks for skeleton-based human action recognition

Cited by 12 publications

References 34 publications

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

Big Data System for Dragon Boat Rowing Action Training Based on Multidimensional Stereo Vision

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