EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition

TL;DR

EITNet is a deep learning framework integrating IoT technology to enhance real-time basketball action recognition accuracy and efficiency, enabling advanced sports analytics and strategic insights.

Contribution

The paper introduces EITNet, a novel IoT-enhanced deep learning architecture combining EfficientDet, I3D, and TimeSformer for improved real-time basketball action recognition.

Findings

Recognition accuracy improved to 92%

Reduced loss to below 5.0 over 50 epochs

Outperformed baseline models in accuracy and efficiency

Abstract

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.