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Article type: Research Article
Authors: Bakhat, Khusha | Kifayat, Kashifa; * | Islam, M. Shujahb | Islam, M. Mattahc
Affiliations: [a] Air University, Islamabad, Pakistan | [b] Anhui Agricultural University, Hefei, Anhui, PR China | [c] National University of Computer and Emerging Sciences, Islamabad, Pakistan
Correspondence: [*] Corresponding author. Kashif Kifayat, Air University, Islamabad, Pakistan. E-mail: kashif.kifayat@mail.au.edu.pk.
Abstract: The method of marking video clips with action symbols is known as vision-based human activity recognition. Robust solutions to this problem have a variety of practical implementations. Due to differences in motion performance, recording environments, and inter-personal differences, the challenge is difficult. We specifically resolve these problems in this study work, and we solve imitations of state-of-the-art research. Projected human activity recognition is based on an amalgamation of CEV & SGM features. The proposed solution outperforms current models and produces state-of-the-art outcomes as compared to the best effectiveness of the control, according to experimental results on the datasets.
Keywords: Complex networks, entropy, human activity recognition, human action recognition, CEV, SGM
DOI: 10.3233/JIFS-213514
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7351-7362, 2022
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