Edge Computing-Based Video Action Recognition Method and Its Application in Online Physical Education Teaching
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Le résumé fourni par la source
Due to the impact of COVID-19, online physical education (PE) teaching has garnered increasing attention. Given the characteristics of online PE teaching, introducing artificial intelligence technology to automatically detect or recognize students’ actions or behaviors has gradually emerged as a trend. However, traditional cloud computing-based intelligent online PE teaching systems often face various challenging issues, such as computational complexity and latency. Edge computing can address these problems. However, edge devices typically have limited computing power, while existing deep action recognition models often contain a large number of parameters and require significant computational resources, making them difficult to deploy on edge devices. To address the above issues, this paper proposes a lightweight video recognition method, named the lightweight video ViT (LWV-ViT) network. More specifically, based on the standard ViT model, the video-based ViT (VBViT) network is first introduced by developing a cross-temporal token interaction module to effectively process temporal information in videos. Furthermore, the LWV-ViT network is proposed by implementing a spatial-temporal pruning scheme to reduce the number of parameters. Finally, the proposed LWV-ViT network is deployed in an edge computing-based online PE teaching system, where it is installed on each edge device. This setup enables fast data processing, reduces transmission latency, and protects sensitive data. Experimental results show that the proposed LWV-ViT network achieves the best recognition rates for both behavior detection (96.5%, 95.73%) and action recognition (97.9%, 88.3%, 79.9%) tasks, and has the fewest trainable parameters (2.7 M), which means it performs well in edge computing-based online PE teaching systems.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Edge Computing-Based Video Action Recognition Method and Its Application in Online Physical Education Teaching
- Date Crossref
- 01/01/2024
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Jilin Agricultural University pays non établi dans la noticeUniversité ou école supérieure
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Jilin International Studies University pays non établi dans la noticeUniversité ou école supérieure
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Shenzhen Technology University pays non établi dans la noticeUniversité ou école supérieure
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Hunan Police Academy pays non établi dans la noticeUniversité ou école supérieure
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Liaoning Police College pays non établi dans la noticeUniversité ou école supérieure
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International Football Education College pays non établi dans la noticeUniversité ou école supérieure
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College of Sport and Art pays non établi dans la noticeUniversité ou école supérieure
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Liaoning Police Academy pays non établi dans la noticeInstitution
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Liaoning Police Academic pays non établi dans la noticeInstitution
Jilin Agricultural University, Jilin International Studies University et Shenzhen Technology University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.