Lightweight and efficient skeleton-based sports activity recognition with ASTM-Net.
Human Activity Recognition (HAR) plays a pivotal role in video understanding, with applications ranging from surveillance to virtual reality. Skeletal data has emerged as a robust modality for HAR, overcoming challenges such as noisy backgrounds and lighting variations. However, current Graph Convol...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0324605 |
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