An enhanced spatial-temporal graph convolution network with high order features for skeleton-based action recognition.
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| Title: | An enhanced spatial-temporal graph convolution network with high order features for skeleton-based action recognition. |
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| Authors: | Al-Hakimi MH; Department of Computer Science, University of Peshawar, Peshawar, Pakistan.; Department of Computer Science, Hodeida University, Hodeida, Yemen., Ahmed I; Department of Computer Science, University of Peshawar, Peshawar, Pakistan., Haseeb M; Department of Computer Science, University of Peshawar, Peshawar, Pakistan., Rassem TH Senior Member IEEE; School of Computer Science and Informatics, De Montfort University, Leicester, United Kingdom., Quradaa FH; Department of Computer Science, University of Peshawar, Peshawar, Pakistan.; Department of Computer Science, Aden Community College, Aden, Yemen., Almoqbily RS; Department of Computer Science, University of Peshawar, Peshawar, Pakistan.; Department of Computer Science, Aden Community College, Aden, Yemen. |
| Source: | PloS one [PLoS One] 2025 Oct 09; Vol. 20 (10), pp. e0332815. Date of Electronic Publication: 2025 Oct 09 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0332815 |
