Summary of Eitnet: An Iot-enhanced Framework For Real-time Basketball Action Recognition, by Jingyu Liu et al.
EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition
by Jingyu Liu, Xinyu Liu, Mingzhe Qu, Tianyi Lyu
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed EITNet model is a deep learning framework that combines multiple techniques to enhance basketball action recognition in real-time environments. It integrates EfficientDet for object detection, I3D for spatiotemporal feature extraction, TimeSformer for temporal analysis, and IoT technology for seamless data collection and processing. The model achieves 92% recognition accuracy, surpassing the baseline EfficientDet model’s 87%, and reduces loss to below 5.0 over 50 epochs. This improvement is crucial in automated sports analysis, enabling the optimization of player performance and strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The EITNet model helps make basketball more efficient by recognizing actions like shots and passes in real-time. It uses a special combination of computer vision techniques to improve accuracy and speed. The model’s results show that it can recognize 92% of basketball actions correctly, which is better than other models. This means coaches and players can use the information provided by EITNet to make better decisions during games. |
Keywords
» Artificial intelligence » Deep learning » Feature extraction » Object detection » Optimization » Spatiotemporal
