Summary of Iot-based 3d Pose Estimation and Motion Optimization For Athletes: Application Of C3d and Openpose, by Fei Ren et al.
IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose
by Fei Ren, Chao Ren, Tianyi Lyu
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes the IoT-Enhanced Pose Optimization Network (IE-PONet) for high-precision 3D pose estimation and motion optimization of track and field athletes. IE-PONet combines C3D for spatiotemporal feature extraction, OpenPose for real-time keypoint detection, and Bayesian optimization for hyperparameter tuning. Experimental results on NTURGB+D and FineGYM datasets show superior performance, with AP^p50 scores of 90.5 and 91.0, and mAP scores of 74.3 and 74.0, respectively. Ablation studies confirm the essential roles of each module in enhancing model accuracy. IE-PONet provides a robust tool for athletic performance analysis and optimization, offering precise technical insights for training and injury prevention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new network that helps athletes improve their sports performances. The network uses special computer vision techniques to track the athlete’s movements and predict their future actions. It works by combining three different tools: C3D, OpenPose, and Bayesian optimization. When tested on two different datasets, this network showed great results in predicting athletic poses and motions. This could help coaches and athletes make better decisions about training and injury prevention. |
Keywords
» Artificial intelligence » Feature extraction » Hyperparameter » Optimization » Pose estimation » Precision » Spatiotemporal
