Summary of Real-time Fitness Exercise Classification and Counting From Video Frames, by Riccardo Riccio
Real-Time Fitness Exercise Classification and Counting from Video Frames
by Riccardo Riccio
First submitted to arxiv on: 18 Nov 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 paper presents a novel approach for real-time exercise classification using BiLSTM neural networks. The proposed method tackles limitations of existing approaches, which rely on synthetic datasets, sensitive raw coordinate inputs, and neglect temporal dependencies in exercise movements. This leads to reduced generalizability and robustness in real-world conditions with varying lighting, camera angles, and user body types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to recognize exercises in real-time using special computer models. The old ways of doing this were not very good because they used fake data, relied on precise measurements that changed depending on who was exercising and how the cameras were set up, and didn’t take into account how people move during exercise. |
Keywords
» Artificial intelligence » Classification
