EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition
This work addresses the need for better automated sports analytics for coaches and analysts, though it is incremental as it combines existing methods with IoT enhancements.
The paper tackled the problem of low accuracy and efficiency in real-time basketball action recognition by proposing EITNet, a deep learning framework integrated with IoT technology, which improved recognition accuracy to 92% and reduced loss to below 5.0 compared to a baseline model.
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.