用物联网+深度学习实时识别篮球动作，准确率达92%。
EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition
- 融合EfficientDet、I3D与TimeSformer，结合物联网实现实时数据采集
- 准确率提升至92%，损失降低至5.0以下，优于基线模型
- 适合体育分析、智能裁判系统及训练优化场景
将物联网技术融入篮球动作识别可提升运动分析能力，为球员表现与战术策略提供关键洞察。然而，现有方法在复杂实时环境中常因遮挡或交互复杂导致精度与效率不足。为此，我们提出EITNet模型，结合EfficientDet进行目标检测、I3D提取时空特征、TimeSformer进行时间分析，并集成物联网技术实现无缝实时数据采集与处理。实验表明，该模型识别准确率达92%，显著优于基线EfficientDet的87%；经过50轮训练，损失降至5.0以下，较EfficientDet的9.0明显下降。物联网集成进一步增强了实时数据分析能力，可动态反馈球员表现与战术信息。论文详述了EITNet的设计、实现与全面评估，验证其在自动化体育分析与数据利用优化方面的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。