EITNet: An IoT-enhanced framework for real-time basketball action recognition
Integrating IoT technology into basketball action recognition enhances sports analytics, providing crucial insights into player performance and game strategy. However, existing methods often fall short in terms of accuracy and efficiency, particularly in complex, real-time environments where player...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824010706 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841553793135673344 |
|---|---|
| author | Jingyu Liu Xinyu Liu Mingzhe Qu Tianyi Lyu |
| author_facet | Jingyu Liu Xinyu Liu Mingzhe Qu Tianyi Lyu |
| author_sort | Jingyu Liu |
| collection | DOAJ |
| description | Integrating IoT technology into basketball action recognition enhances sports analytics, providing crucial insights into player performance and game strategy. However, existing methods often fall short in terms of accuracy and efficiency, particularly in complex, real-time environments where player movements are frequently occluded or involve intricate interactions. To overcome these challenges, we propose the EITNet model, a deep learning framework that combines EfficientDet for object detection, I3D for spatiotemporal feature extraction, and TimeSformer for temporal analysis, all integrated with IoT technology for seamless real-time data collection and processing. Our contributions include developing a robust architecture that improves recognition accuracy to 92%, surpassing the baseline EfficientDet model’s 87%, and reducing loss to below 5.0 compared to EfficientDet’s 9.0 over 50 epochs. Furthermore, the integration of IoT technology enhances real-time data processing, providing adaptive insights into player performance and strategy. The paper details the design and implementation of EITNet, experimental validation, and a comprehensive evaluation against existing models. The results demonstrate EITNet’s potential to significantly advance automated sports analysis and optimize data utilization for player performance and strategy improvement. |
| format | Article |
| id | doaj-art-0a2e273886c045b4abf5dea4cf77f13e |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-0a2e273886c045b4abf5dea4cf77f13e2025-01-09T06:13:16ZengElsevierAlexandria Engineering Journal1110-01682025-01-01110567578EITNet: An IoT-enhanced framework for real-time basketball action recognitionJingyu Liu0Xinyu Liu1Mingzhe Qu2Tianyi Lyu3Henan Sport University, zhengzhou, 450000, China; Henan University, Kaifeng 475000, ChinaHenan Sport University, zhengzhou, 450000, ChinaHenan Sport University, zhengzhou, 450000, China; Corresponding author.Granite Telecommunications LLC. 100 Newport Avenue Extension, Quincy, MA, 02171, USAIntegrating IoT technology into basketball action recognition enhances sports analytics, providing crucial insights into player performance and game strategy. However, existing methods often fall short in terms of accuracy and efficiency, particularly in complex, real-time environments where player movements are frequently occluded or involve intricate interactions. To overcome these challenges, we propose the EITNet model, a deep learning framework that combines EfficientDet for object detection, I3D for spatiotemporal feature extraction, and TimeSformer for temporal analysis, all integrated with IoT technology for seamless real-time data collection and processing. Our contributions include developing a robust architecture that improves recognition accuracy to 92%, surpassing the baseline EfficientDet model’s 87%, and reducing loss to below 5.0 compared to EfficientDet’s 9.0 over 50 epochs. Furthermore, the integration of IoT technology enhances real-time data processing, providing adaptive insights into player performance and strategy. The paper details the design and implementation of EITNet, experimental validation, and a comprehensive evaluation against existing models. The results demonstrate EITNet’s potential to significantly advance automated sports analysis and optimize data utilization for player performance and strategy improvement.http://www.sciencedirect.com/science/article/pii/S1110016824010706EITNetIoTBasketball action recognitionSpatiotemporal featuresReal-time processing |
| spellingShingle | Jingyu Liu Xinyu Liu Mingzhe Qu Tianyi Lyu EITNet: An IoT-enhanced framework for real-time basketball action recognition Alexandria Engineering Journal EITNet IoT Basketball action recognition Spatiotemporal features Real-time processing |
| title | EITNet: An IoT-enhanced framework for real-time basketball action recognition |
| title_full | EITNet: An IoT-enhanced framework for real-time basketball action recognition |
| title_fullStr | EITNet: An IoT-enhanced framework for real-time basketball action recognition |
| title_full_unstemmed | EITNet: An IoT-enhanced framework for real-time basketball action recognition |
| title_short | EITNet: An IoT-enhanced framework for real-time basketball action recognition |
| title_sort | eitnet an iot enhanced framework for real time basketball action recognition |
| topic | EITNet IoT Basketball action recognition Spatiotemporal features Real-time processing |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824010706 |
| work_keys_str_mv | AT jingyuliu eitnetaniotenhancedframeworkforrealtimebasketballactionrecognition AT xinyuliu eitnetaniotenhancedframeworkforrealtimebasketballactionrecognition AT mingzhequ eitnetaniotenhancedframeworkforrealtimebasketballactionrecognition AT tianyilyu eitnetaniotenhancedframeworkforrealtimebasketballactionrecognition |