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

Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2, Miguel Rio1

1 Electronic and Electrical Engineering Department, University College London, London, WC1E7JE, England
2 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt

* Corresponding Author: Motasem S. Alsawadi. Email: email

(This article belongs to the Special Issue: Optimization Algorithm for Intelligent Computing Application)

Intelligent Automation & Soft Computing 2023, 37(3), 2767-2782. https://doi.org/10.32604/iasc.2023.039440

Abstract

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 signed-rank test, histogram, and times analysis.

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APA Style
Alsawadi, M.S., El-kenawy, E.M., Rio, M. (2023). Advanced guided whale optimization algorithm for feature selection in blazepose action recognition. Intelligent Automation & Soft Computing, 37(3), 2767-2782. https://doi.org/10.32604/iasc.2023.039440
Vancouver Style
Alsawadi MS, El-kenawy EM, Rio M. Advanced guided whale optimization algorithm for feature selection in blazepose action recognition. Intell Automat Soft Comput . 2023;37(3):2767-2782 https://doi.org/10.32604/iasc.2023.039440
IEEE Style
M.S. Alsawadi, E.M. El-kenawy, and M. Rio "Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition," Intell. Automat. Soft Comput. , vol. 37, no. 3, pp. 2767-2782. 2023. https://doi.org/10.32604/iasc.2023.039440



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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