Summary of Autosoccerpose: Automated 3d Posture Analysis Of Soccer Shot Movements, by Calvin Yeung et al.
AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements
by Calvin Yeung, Kenjiro Ide, Keisuke Fujii
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses the limitations of current computer vision models for image understanding, particularly in sports analysis. The authors introduce the 3D Shot Posture (3DSP) dataset, a comprehensive collection of soccer broadcast videos with 2D pose annotations. To analyze these sequences, they propose the 3DSP-GRAE model, a non-linear approach that embeds pose sequences. Additionally, they present AutoSoccerPose, a pipeline for semi-automating 2D and 3D pose estimation and posture analysis. The authors validate their approaches on SoccerNet and 3DSP datasets, demonstrating their effectiveness in capturing complex spatiotemporal relationships. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding soccer player postures from video recordings. Right now, it’s hard to analyze these postures because we don’t have enough data or the right tools. The authors created a big dataset with 2D pose information and developed new models that can learn complex patterns in posture sequences. They tested their ideas on two datasets and showed that they work well. |
Keywords
» Artificial intelligence » Pose estimation » Spatiotemporal
