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Improved Shark Smell Optimization Algorithm for Human Action Recognition

Inzamam Mashood Nasir1,*, Mudassar Raza1, Jamal Hussain Shah1, Muhammad Attique Khan2, Yun-Cheol Nam3, Yunyoung Nam4,*

1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan
2 Department of Computer Science, HITEC University, Taxila, Pakistan
3 Department of Architecture, Joongbu University, Goyang, 10279, South Korea
4 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea

* Corresponding Authors: Inzamam Mashood Nasir. Email: email; Yunyoung Nam. Email: email

(This article belongs to the Special Issue: Recent Advances in Hyper Parameters Optimization, Features Optimization, and Deep Learning for Video Surveillance and Biometric Applications)

Computers, Materials & Continua 2023, 76(3), 2667-2684. https://doi.org/10.32604/cmc.2023.035214

Abstract

Human Action Recognition (HAR) in uncontrolled environments targets to recognition of different actions from a video. An effective HAR model can be employed for an application like human-computer interaction, health care, person tracking, and video surveillance. Machine Learning (ML) approaches, specifically, Convolutional Neural Network (CNN) models had been widely used and achieved impressive results through feature fusion. The accuracy and effectiveness of these models continue to be the biggest challenge in this field. In this article, a novel feature optimization algorithm, called improved Shark Smell Optimization (iSSO) is proposed to reduce the redundancy of extracted features. This proposed technique is inspired by the behavior of white sharks, and how they find the best prey in the whole search space. The proposed iSSO algorithm divides the Feature Vector (FV) into subparts, where a search is conducted to find optimal local features from each subpart of FV. Once local optimal features are selected, a global search is conducted to further optimize these features. The proposed iSSO algorithm is employed on nine (9) selected CNN models. These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition. To evaluate the model, two publicly available datasets UCF-Sports and Hollywood2 are selected.

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APA Style
Nasir, I.M., Raza, M., Shah, J.H., Khan, M.A., Nam, Y. et al. (2023). improved shark smell optimization algorithm for human action recognition. Computers, Materials & Continua, 76(3), 2667-2684. https://doi.org/10.32604/cmc.2023.035214
Vancouver Style
Nasir IM, Raza M, Shah JH, Khan MA, Nam Y, Nam Y. improved shark smell optimization algorithm for human action recognition. Comput Mater Contin. 2023;76(3):2667-2684 https://doi.org/10.32604/cmc.2023.035214
IEEE Style
I.M. Nasir, M. Raza, J.H. Shah, M.A. Khan, Y. Nam, and Y. Nam " Improved Shark Smell Optimization Algorithm for Human Action Recognition," Comput. Mater. Contin., vol. 76, no. 3, pp. 2667-2684. 2023. https://doi.org/10.32604/cmc.2023.035214



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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